From 82507a2b89da726fac25692faae6591b9e5f9aab Mon Sep 17 00:00:00 2001 From: Jonas Weinz Date: Thu, 2 Jan 2020 18:14:02 +0100 Subject: [PATCH] Initialization done, Recipe to text --- .../EvolutionaryAlgorithm.ipynb | 4612 ++++++++++------- .../AdjacencyMatrixRefinement.ipynb | 23 +- RecipeAnalysis/MatrixGeneration.ipynb | 135 +- RecipeAnalysis/Recipe.ipynb | 10 +- RecipeAnalysis/Recipe.py | 10 +- Vocabulary/Vocabulary Creation.ipynb | 6 +- 6 files changed, 2743 insertions(+), 2053 deletions(-) diff --git a/EvolutionaryAlgorithm/EvolutionaryAlgorithm.ipynb b/EvolutionaryAlgorithm/EvolutionaryAlgorithm.ipynb index b4a0ab9..6519a93 100644 --- a/EvolutionaryAlgorithm/EvolutionaryAlgorithm.ipynb +++ b/EvolutionaryAlgorithm/EvolutionaryAlgorithm.ipynb @@ -465,6 +465,9 @@ "metadata": {}, "outputs": [], "source": [ + "PREPARE_RATIO_THRESHOLD = 0.35\n", + "HEAT_RATIO_THRESHOLD = 0.65\n", + "\n", "def prepare_ratio(ing:str):\n", " keys, values = m_grouped_act.get_backward_adjacent(Ingredient(ing).to_json())\n", " action_dict = dict(zip(keys,values))\n", @@ -475,14 +478,19 @@ " returns randomly a boolean value if ing should be prepared, w.r.t. the prepare_ration function\n", " \"\"\"\n", " \n", - " return prepare_ratio(ing) > np.random.normal(0.35,0.1)\n", + " return prepare_ratio(ing) > np.random.normal(PREPARE_RATIO_THRESHOLD,0.1)\n", "\n", - "def random_heated(ingredient:str):\n", + "def heat_ratio(ingredient:str):\n", " action_set, action_weights = m_grouped_base_act.get_backward_adjacent(ingredient)\n", " d = dict(zip(action_set, action_weights))\n", " ratio = 1 - d['prepare'] / d['heat']\n", " \n", - " return ratio > np.random.normal(0.65,0.15)\n" + " return ratio\n", + "\n", + "def random_heated(ingredient:str):\n", + " ratio = heat_ratio(ingredient)\n", + " \n", + " return ratio > np.random.normal(HEAT_RATIO_THRESHOLD,0.15)\n" ] }, { @@ -523,7 +531,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 19, "metadata": {}, "outputs": [], "source": [ @@ -549,6 +557,73 @@ " return keys[sort], normalized_values[sort]" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Helper class for instructions" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "class RecipeInstructionState(object):\n", + " def __init__(self):\n", + " self.current_step = 1\n", + " self.id_to_state = {}\n", + " self.instructions_by_step = {}\n", + " self.step_by_nodeid = {}\n", + " self.text_by_nodeid = {}\n", + " self.ingredients = set()\n", + " \n", + " def _add_instruction(self, node_id):\n", + " s = self.text_by_nodeid[node_id]\n", + " self.instructions_by_step[self.current_step] = s\n", + " self.step_by_nodeid[node_id] = self.current_step\n", + " self.current_step += 1\n", + " return self.current_step - 1\n", + " \n", + " def add_text(self, node_id, text, is_instruction=False, is_ingredient=False):\n", + " self.text_by_nodeid[node_id] = text\n", + " if is_instruction:\n", + " self._add_instruction(node_id)\n", + " if is_ingredient:\n", + " self.ingredients.add(text)\n", + " \n", + " def exists_any_instruction(self, node_ids:list):\n", + " \"\"\"check if any instruction exists for list of id's\n", + " \"\"\"\n", + " \n", + " for node_id in node_ids:\n", + " if node_id in self.step_by_nodeid:\n", + " return True\n", + " return False\n", + " \n", + " def to_markdown(self):\n", + " \n", + " md_text = \"**Ingredients**:\\n\"\n", + " \n", + " for ing in self.ingredients:\n", + " md_text += f\" * {ing}\\n\"\n", + " \n", + " md_text += \"\\n\\n**Instructions**:\\n\\n\"\n", + " md_text += \"| Step | Instruction |\\n\"\n", + " md_text += \"| ----:|:----------- |\\n\"\n", + " \n", + " for step in range(1, self.current_step):\n", + " md_text += f\"| {step} | {self.instructions_by_step[step]} |\\n\"\n", + " \n", + " return Markdown(md_text)\n", + " \n", + " \n", + " \n", + " \n", + " " + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -559,7 +634,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 21, "metadata": {}, "outputs": [], "source": [ @@ -728,6 +803,11 @@ " \n", " return d\n", " \n", + " def simplify(self):\n", + " # simplify nodes (mainly used to delete doubled Mix Nodes)\n", + " for c in self.childs().copy():\n", + " c.simplify()\n", + " \n", " def serialize(self):\n", " r = {}\n", " r['type'] = str(self.__class__.__name__)\n", @@ -742,6 +822,10 @@ " \n", " def node_score(self):\n", " raise NotImplementedError()\n", + " \n", + " def to_instruction(self, state:RecipeInstructionState):\n", + " # create an instruction out of a recipe Tree\n", + " raise NotImplementedError()\n", " \n" ] }, @@ -761,7 +845,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 22, "metadata": {}, "outputs": [], "source": [ @@ -870,6 +954,87 @@ " #return 0.5 * (s_base + s)\n", " return s\n", " \n", + " def simplify(self):\n", + " for c in self.childs().copy():\n", + " c.simplify()\n", + " \n", + " # if our parent is also a Mix Node, we can just delete ourselve\n", + " p = self.parent()\n", + " \n", + " if p is not None:\n", + " if type(p) == MixNode:\n", + " # just delete ourselve\n", + " self.remove()\n", + " \n", + " def to_instruction(self, state:RecipeInstructionState = None):\n", + " \"\"\"\n", + " returns a RecipeInstructionState\n", + " \"\"\"\n", + " \n", + " def english_enum(items, use_and=True):\n", + " if len(items) > 1 and use_and:\n", + " return \", \".join(items[:-1]) + \" and \" + items[-1]\n", + " return \", \".join(items)\n", + " \n", + " if state is None:\n", + " state = RecipeInstructionState()\n", + " \n", + " for c in self.childs():\n", + " c.to_instruction(state)\n", + " \n", + " \n", + " text = \"\"\n", + " \n", + " # children with instructions\n", + " instruction_childs = []\n", + " \n", + " # children without instructions\n", + " base_childs = []\n", + " \n", + " # childre without instructions that are ingredients\n", + " ingredient_childs = []\n", + " \n", + " for c in self.childs():\n", + " assert type(c) != MixNode\n", + " if type(c) == IngredientNode:\n", + " ingredient_childs.append(c)\n", + " elif c._id not in state.step_by_nodeid:\n", + " # action node with no step so far, so a base child\n", + " base_childs.append(c)\n", + " else:\n", + " instruction_childs.append(c)\n", + " \n", + " if len(base_childs) > 0:\n", + " use_and= len(ingredient_childs)==0 and len(instruction_childs)==0\n", + " text = english_enum([state.text_by_nodeid[c._id] for c in base_childs], use_and=use_and)\n", + " \n", + " \n", + " if len(ingredient_childs) > 0:\n", + " if len(base_childs) > 0:\n", + " text += \" and mix it with \" + english_enum([state.text_by_nodeid[c._id] for c in ingredient_childs])\n", + " \n", + " else:\n", + " text = \"Mix \" + english_enum([state.text_by_nodeid[c._id] for c in ingredient_childs])\n", + " \n", + " if len(instruction_childs) > 0:\n", + " if len(base_childs) == 0:\n", + " text = \"Mix together the results of \"\n", + " else:\n", + " text += \" and mix it together with the results of \"\n", + " \n", + " text += english_enum([f\"step {state.step_by_nodeid[c._id]}\" for c in instruction_childs])\n", + " \n", + " text += \".\"\n", + " \n", + " if type(self.parent()) == ActionNode:\n", + " state.add_text(self._id, text, is_instruction=False)\n", + " else:\n", + " state.add_text(self._id, text, is_instruction=True)\n", + " \n", + " \n", + " return state\n", + " \n", + " \n", " \n", " \n" ] @@ -883,10 +1048,13 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 41, "metadata": {}, "outputs": [], "source": [ + "n_wanted_actions = 2\n", + "gaussian_normalize_factor = 1 / gaussian(n_wanted_actions, n_wanted_actions, 1)\n", + "\n", "class IngredientNode(RecipeTreeNode):\n", " def __init__(self, name, constant=False):\n", " super().__init__(name, constant, single_child=True)\n", @@ -901,18 +1069,49 @@ " return a_list\n", " \n", " def mutate_node(self):\n", - " self._name = random.choice(base_ingredients)\n", + " pass\n", + " #self._name = random.choice(base_ingredients)\n", " #TODO: change w.r.t. mixing probabilities \n", " \n", " def traverse_ingredients(self):\n", " return [Ingredient(self._name)]\n", " \n", + " def duplicate_actions_score(self, actions):\n", + " \n", + " if len(actions) == 0:\n", + " return 1\n", + " \n", + " seen_actions = set()\n", + " n_duplicates = 0\n", + " for act in actions:\n", + " if act in seen_actions:\n", + " n_duplicates += 1\n", + " else:\n", + " seen_actions.add(act)\n", + " \n", + " duplicate_actions_score = len(seen_actions) / len(actions)\n", + " \n", + " return duplicate_actions_score\n", + " \n", " def node_score(self):\n", " actions = self.get_actions()\n", " \n", " if len(actions) == 0:\n", " if p_ingredient_unprepared(self._name) < 0.2:\n", " return 0\n", + " else:\n", + " if p_ingredient_unprepared(self._name) > 0.3:\n", + " return 0\n", + " \n", + " action_number_score = gaussian(len(set(actions)), n_wanted_actions, 1) * gaussian_normalize_factor\n", + " return action_number_score * self.duplicate_actions_score(actions)\n", + " \n", + " \"\"\"\n", + " actions = self.get_actions()\n", + " \n", + " if len(actions) == 0:\n", + " if p_ingredient_unprepared(self._name) < 0.2:\n", + " return 0\n", " return 1\n", " \n", " seen_actions = set()\n", @@ -926,10 +1125,15 @@ " duplicate_actions_score = len(seen_actions) / len(actions)\n", " \n", " return duplicate_actions_score\n", + " \"\"\"\n", " \n", " \n", " def dot_node(self, dot):\n", - " dot.node(self._id, label=f\"< {self._name}
node score:{self.node_score():.4f}>\", shape=\"box\", style=\"filled\", color=\"#ffe6cc\")" + " dot.node(self._id, label=f\"< {self._name}
node score:{self.node_score():.4f}>\", shape=\"box\", style=\"filled\", color=\"#ffe6cc\")\n", + " \n", + " def to_instruction(self, state:RecipeInstructionState = None):\n", + " state.add_text(self._id, self._name, is_instruction=False, is_ingredient=True)\n", + " return state" ] }, { @@ -941,7 +1145,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 42, "metadata": {}, "outputs": [], "source": [ @@ -974,6 +1178,20 @@ " \n", " for ing in ings:\n", " try:\n", + " \n", + " i_act = m_act._x_label_index[self.name()]\n", + " i_ing = m_act._y_label_index[ing.to_json()]\n", + " \n", + " if c_act[i_act,i_ing] > 0:\n", + " s += 1\n", + " \n", + " except KeyError as e:\n", + " #print(f\"WARNING: no entry found for: {str(e)}\")\n", + " pass\n", + " \n", + " ''' # old method:\n", + " for ing in ings:\n", + " try:\n", " #score = asym_score(self._name, ing.to_json(), m_act, c_act)\n", " #base_score = asym_score(self._name, ing._base_ingredient, m_base_act, c_base_act)\n", " \n", @@ -982,12 +1200,38 @@ " s += score\n", " except KeyError as e:\n", " pass\n", + " '''\n", " \n", " \n", " return s / len(ings)\n", " \n", " def dot_node(self, dot):\n", - " dot.node(self._id, label=f\"< {self._name}
node score: {self.node_score():.4f}>\", shape=\"ellipse\", style=\"filled\", color=\"#dae8fc\")" + " dot.node(self._id, label=f\"< {self._name}
node score: {self.node_score():.4f}>\", shape=\"ellipse\", style=\"filled\", color=\"#dae8fc\")\n", + " \n", + " def to_instruction(self, state:RecipeInstructionState = None):\n", + " \n", + " if state is None:\n", + " state = RecipeInstructionState()\n", + " \n", + " for c in self.childs():\n", + " c.to_instruction(state)\n", + " \n", + " c = self._child\n", + " \n", + " if type(c) == MixNode:\n", + " text = state.text_by_nodeid[c._id] + f\" Then {self._name} it.\"\n", + " state.add_text(self._id, text, True)\n", + " elif type(c) == IngredientNode:\n", + " text = f\"{self._name} {state.text_by_nodeid[c._id]}\"\n", + " state.add_text(self._id, text, False)\n", + " \n", + " elif type(c) == ActionNode:\n", + " assert c._id in state.step_by_nodeid\n", + " text = f\"{self._name} the result of step {state.step_by_nodeid[c._id]}\"\n", + " state.add_text(self._id, text, True)\n", + " \n", + " return state\n", + " \n" ] }, { @@ -999,153 +1243,137 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 43, "metadata": {}, "outputs": [], "source": [ "class Tree(object):\n", " @staticmethod\n", - " def build_initial_tree(ingredients: list, main_ingredients: list, max_n = 4, wheel_turns = 2):\n", + " def build_initial_tree(ingredients: list, main_ingredients: list, max_n = 5, wheel_turns = 2):\n", " \n", - " '''\n", - " # get action sets for ingredients\n", - " possible_actions = {}\n", - " for ing in ingredients:\n", - " action_set, action_weights = m_base_act.get_backward_adjacent(ing)\n", - " possible_actions[ing] = set(action_set.tolist()[:5])\n", - " \n", - " # now find actions with the same subset\n", - " \n", - " ings_for_acts = {}\n", - " \n", - " for ing, acts in possible_actions.items():\n", - " for a in acts:\n", - " if a not in ings_for_acts:\n", - " ings_for_acts[a] = set()\n", - " \n", - " ings_for_acts[a].add(ing)\n", + " assert set(main_ingredients).issubset(set(ingredients))\n", + "\n", + " def does_action_match(ingredient:str, action:str, t = 0.6):\n", + " return relative_action_rank(ingredient, action) > t\n", + "\n", "\n", - " '''\n", - " \n", " # choose randomly an action for each ingredient by the \"wheel of fortune\" method\n", " actions_for_ing = {}\n", " for ing in ingredients:\n", " actions_for_ing[ing] = set()\n", " action_set, action_weights = m_base_act.get_backward_adjacent(ing)\n", - " for i in range(wheel_turns):\n", - " action = ea_tools.wheel_of_fortune_selection(action_set[:max_n], action_weights[:max_n])\n", - " actions_for_ing[ing].add(action)\n", + " if random_heated(ing):\n", + " #print(action_set)\n", + " action_set, action_weights = filter_set_by_group(action_set, action_weights, \"heat\")\n", + " #print(action_set)\n", + " for i in range(wheel_turns):\n", + " if ing in main_ingredients:\n", + " # if main ingredient: choose by action probability\n", + " w = np.array(list(action_weights), dtype=float)\n", + " w *= (1.0 / np.sum(w))\n", + " action = np.random.choice(list(action_set), size=1, replace=False, p=w)[0]\n", + " else:\n", + " # else: choose rank based\n", + " action = ea_tools.wheel_of_fortune_selection(action_set[:max_n], action_weights[:max_n])\n", + " actions_for_ing[ing].add(action)\n", + " #print(f\"action {action} for ing {ing}\")\n", " #print(ing, action)\n", "\n", - " ings_for_acts = {}\n", - " \n", - " for ing, acts in actions_for_ing.items():\n", - " for a in acts:\n", - " if a not in ings_for_acts:\n", - " ings_for_acts[a] = set()\n", - " \n", - " ings_for_acts[a].add(ing)\n", - " \n", - " # now looking for the largest subset and choose one of them randomly\n", - " \n", - " action_keys = np.array(list(ings_for_acts.keys()))\n", - " set_lengths = np.array([len(ings_for_acts[a]) for a in action_keys])\n", - " \n", - " # sort lengths\n", - " sorted_length_indices = np.argsort(-set_lengths)\n", - " \n", - " # now perform the following steps:\n", - " # * go through all unprocessed ingredients\n", - " # * for each ing: find largest action set that is suitable\n", - " # * perform this action on all it's ingredients.\n", - " # * continue until no ingredient is left\n", - " \n", - " unprocessed_ings = set(ingredients)\n", - " unprocessed_actions = set(ings_for_acts.keys())\n", - " \n", + " # create ingredient nodes:\n", " ingredient_nodes = {}\n", - " \n", + "\n", " # create ingredient nodes:\n", " for ing in ingredients:\n", - " ingredient_nodes[ing] = IngredientNode(ing, constant=True)\n", - " \n", - " i = 0\n", - " \n", - " while len(unprocessed_ings) > 0:\n", - " \n", - " # select random ingredient:\n", - " ing = np.random.choice(list(unprocessed_ings))\n", - " \n", - " sorted_actions = action_keys[sorted_length_indices]\n", - " selected_action = None\n", - " \n", - " for action in sorted_actions:\n", - " if ing in ings_for_acts[action]:\n", - " selected_action = action\n", - " break\n", - " \n", - " # found best action. apply it to all matching ingredients\n", - " if selected_action is not None:\n", - " matching_ingredients = ings_for_acts[selected_action]\n", - " \n", - " # debugging:\n", - " '''\n", - " print(f\"choose {selected_action}\")\n", - " print(f\"matching ingredients {matching_ingredients}\")\n", - " '''\n", - " \n", - " if len(matching_ingredients) == 1:\n", - " ing = list(matching_ingredients)[0]\n", - " ing_node = ingredient_nodes[ing].root()\n", - " action_node = ActionNode(selected_action)\n", - " action_node.add_child(ing_node)\n", + " new_node = IngredientNode(ing, constant=True)\n", + "\n", + " # check if we should do a preparation step\n", + " if random_prepare(ing):\n", + " # choose a preparation cooking action\n", + " action_set, action_weights = m_act.get_backward_adjacent(Ingredient(ing).to_json())\n", + " action_set, action_weights = filter_set_by_group(action_set, action_weights, \"prepare\")\n", + " if len(action_set) > 0:\n", + " action = ea_tools.wheel_of_fortune_selection(action_set[:max_n], action_weights[:max_n])\n", + " act_node = ActionNode(action)\n", + " act_node.add_child(new_node)\n", + " new_node = act_node\n", + "\n", + "\n", + " ingredient_nodes[ing] = new_node\n", + "\n", + " # starting now with the actions found for the main ingredients and try to match all ingredients together\n", + " # with that:\n", + "\n", + " unprocessed_ings = set(filter(lambda x: len(actions_for_ing[x]) > 0, ingredients))\n", + " unprocessed_main_ings = set(filter(lambda x: len(actions_for_ing[x]) > 0, main_ingredients))\n", + "\n", + " while len(unprocessed_main_ings) > 0:\n", + " main_ing = unprocessed_main_ings.pop()\n", + "\n", + " # random action for that ing:\n", + " act = actions_for_ing[main_ing].pop()\n", + "\n", + " act_node = ActionNode(act)\n", + " mix_node = MixNode()\n", + " mix_node.add_child(ingredient_nodes[main_ing])\n", + " act_node.add_child(mix_node)\n", + " ingredient_nodes[main_ing] = act_node\n", + "\n", + " unprocessed_ings.remove(main_ing)\n", + "\n", + " for ing in unprocessed_ings.copy():\n", + " if does_action_match(ing, act):\n", + " mix_node.add_child(ingredient_nodes[ing])\n", + " ingredient_nodes[ing] = act_node\n", " unprocessed_ings.remove(ing)\n", - " #display(action_node.dot())\n", - " \n", - " else:\n", - " \n", - " nodes_to_mix = set()\n", + " if ing in unprocessed_main_ings:\n", + " unprocessed_main_ings.remove(ing)\n", + "\n", + " if len(mix_node.childs()) == 1:\n", + " mix_node.remove()\n", + "\n", + " # now make the same with all remaining ingredients:\n", + " while len(unprocessed_ings) > 0:\n", + " current_ing = unprocessed_ings.pop() \n", + "\n", + " # random action for that ing:\n", + " act = actions_for_ing[current_ing].pop()\n", + "\n", + " act_node = ActionNode(act)\n", + " mix_node = MixNode()\n", + " mix_node.add_child(ingredient_nodes[current_ing])\n", + " act_node.add_child(mix_node)\n", + "\n", + " ingredient_nodes[current_ing] = act_node\n", + "\n", + "\n", + " for ing in unprocessed_ings.copy():\n", + " if does_action_match(ing, act):\n", + " mix_node.add_child(ingredient_nodes[ing])\n", + " ingredient_nodes[ing] = act_node\n", + " unprocessed_ings.remove(ing)\n", + "\n", + " if len(mix_node.childs()) == 1:\n", + " mix_node.remove()\n", "\n", - " mix_node = MixNode()\n", - " action_node = ActionNode(selected_action)\n", - " action_node.add_child(mix_node)\n", - " \n", - " for ing in matching_ingredients:\n", - " nodes_to_mix.add(ingredient_nodes[ing].root())\n", - " \n", - " if ing in unprocessed_ings:\n", - " unprocessed_ings.remove(ing)\n", "\n", - " for node in nodes_to_mix:\n", - " mix_node.add_child(node)\n", - " #display(action_node.dot())\n", - " \n", - " # debugging:\n", - " '''\n", - " tmp = set([n.root() for n in ingredient_nodes.values()])\n", - " print(f\"iteration {i}:\")\n", - " for n in tmp:\n", - " print(n.name())\n", - " display(n.dot())\n", - " '''\n", - " i += 1\n", - " \n", - " \n", " root_layer = set([n.root() for n in ingredient_nodes.values()])\n", "\n", " root_layer_without_parents = []\n", " for node in root_layer:\n", " if node.parent() is None:\n", " root_layer_without_parents.append(node)\n", - " \n", + "\n", " if len(root_layer_without_parents) == 1:\n", - " return root_layer_without_parents[0]\n", - " \n", - " root_node = MixNode()\n", - " for r in root_layer_without_parents:\n", - " root_node.add_child(r)\n", + " root_node = root_layer_without_parents[0]\n", + "\n", + " else:\n", + " root_node = MixNode()\n", + " for r in root_layer_without_parents:\n", + " root_node.add_child(r)\n", " \n", " return root_node\n", + "\n", + "\n", " \n", " @staticmethod\n", " def find_ingredients(constant_ingredients, main_ingredients, min_additional:int, max_additional:int, top_ings:int=3):\n", @@ -1155,15 +1383,40 @@ " '''\n", " \n", " seen_items = set(constant_ingredients)\n", - " \n", + "\n", " items = []\n", " scores = []\n", - " \n", + "\n", + " assert set(main_ingredients).issubset(set(constant_ingredients))\n", + "\n", + " # additional ingredients are choosen w.r.t all given ingredients\n", + " n_additional_ings = np.random.randint(min_additional, max_additional + 1)\n", + "\n", + " # extra ings are ingredients choosen specially for the main ingredient\n", + " n_extra_ings = int((len(main_ingredients) / len(constant_ingredients)) * n_additional_ings)\n", + "\n", + " if n_extra_ings > n_additional_ings:\n", + " n_extra_ings = n_additional_ings\n", + "\n", + "\n", + " # choose extra ingredients\n", + " extra_candidates = []\n", + " extra_weights = []\n", + "\n", + " for ing in main_ingredients:\n", + " candidates, weights = normalized_score(ing, m_base_mix)\n", + " extra_candidates.append(candidates[:10])\n", + " extra_weights.append(weights[:10])\n", + "\n", + " extra_ingredients = ea_tools.combined_wheel_of_fortune_selection(extra_candidates,\n", + " extra_weights,\n", + " n_extra_ings)\n", + "\n", " for ing in constant_ingredients:\n", " # find best matching ingredients\n", " best_items = []\n", " best_scores = []\n", - " \n", + "\n", " candidates, weights = m_base_mix.get_adjacent(ing)\n", " i = 0\n", " while i < len(candidates) and len(best_items) < top_ings:\n", @@ -1171,20 +1424,17 @@ " best_items.append(candidates[i])\n", " best_scores.append(weights[i])\n", " i += 1\n", - " \n", + "\n", " items.append(best_items)\n", " scores.append(best_scores)\n", - " \n", + "\n", " #TODO: error handling if too few options are availabale!\n", - " \n", + "\n", " additional_ingredients = ea_tools.combined_wheel_of_fortune_selection(items,\n", " scores,\n", - " np.random.randint(\n", - " min_additional,\n", - " max_additional + 1\n", - " ))\n", + " n_additional_ings - n_extra_ings)\n", " \n", - " return list(constant_ingredients) + list(additional_ingredients)\n", + " return list(constant_ingredients) + list(additional_ingredients) + list(extra_ingredients)\n", "\n", " @staticmethod\n", " def from_ingredients(ingredients: list, main_ingredients: list, additional_ings=0):\n", @@ -1250,6 +1500,9 @@ " n = random.choices(nodes, weights)[0]\n", " \n", " n.mutate()\n", + " \n", + " # check for simplification after modification\n", + " self.root().simplify()\n", " \n", " def dot(self):\n", " return self.root().dot()\n", @@ -1348,441 +1601,6 @@ " return Tree.from_serialization(self.serialize())\n" ] }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [], - "source": [ - "ingredients = [\"onion\", \"tomato\", \"rice\", \"salt\"] \n", - "main_ingredients = [\"rice\"]\n", - "\n", - "assert set(main_ingredients).issubset(set(ingredients))\n", - "\n", - "max_n = 5\n", - "wheel_turns = 2\n", - "\n", - "def does_action_match(ingredient:str, action:str, t = 0.6):\n", - " return relative_action_rank(ingredient, action) > t\n", - "\n", - "\n", - "# choose randomly an action for each ingredient by the \"wheel of fortune\" method\n", - "actions_for_ing = {}\n", - "for ing in ingredients:\n", - " actions_for_ing[ing] = set()\n", - " action_set, action_weights = m_base_act.get_backward_adjacent(ing)\n", - " if random_heated(ing):\n", - " #print(action_set)\n", - " action_set, action_weights = filter_set_by_group(action_set, action_weights, \"heat\")\n", - " #print(action_set)\n", - " for i in range(wheel_turns):\n", - " if ing in main_ingredients:\n", - " # if main ingredient: choose by action probability\n", - " w = np.array(list(action_weights), dtype=float)\n", - " w *= (1.0 / np.sum(w))\n", - " action = np.random.choice(list(action_set), size=1, replace=False, p=w)[0]\n", - " else:\n", - " # else: choose rank based\n", - " action = ea_tools.wheel_of_fortune_selection(action_set[:max_n], action_weights[:max_n])\n", - " actions_for_ing[ing].add(action)\n", - " #print(f\"action {action} for ing {ing}\")\n", - " #print(ing, action)\n", - " \n", - "# create ingredient nodes:\n", - "ingredient_nodes = {}\n", - "\n", - "# create ingredient nodes:\n", - "for ing in ingredients:\n", - " new_node = IngredientNode(ing, constant=True)\n", - " \n", - " # check if we should do a preparation step\n", - " if random_prepare(ing):\n", - " # choose a preparation cooking action\n", - " action_set, action_weights = m_act.get_backward_adjacent(Ingredient(ing).to_json())\n", - " action_set, action_weights = filter_set_by_group(action_set, action_weights, \"prepare\")\n", - " if len(action_set) > 0:\n", - " action = ea_tools.wheel_of_fortune_selection(action_set[:max_n], action_weights[:max_n])\n", - " act_node = ActionNode(action)\n", - " act_node.add_child(new_node)\n", - " new_node = act_node\n", - " \n", - " \n", - " ingredient_nodes[ing] = new_node\n", - "\n", - "# starting now with the actions found for the main ingredients and try to match all ingredients together\n", - "# with that:\n", - "\n", - "unprocessed_ings = set(filter(lambda x: len(actions_for_ing[x]) > 0, ingredients))\n", - "unprocessed_main_ings = set(filter(lambda x: len(actions_for_ing[x]) > 0, main_ingredients))\n", - "\n", - "while len(unprocessed_main_ings) > 0:\n", - " main_ing = unprocessed_main_ings.pop()\n", - " \n", - " # random action for that ing:\n", - " act = actions_for_ing[main_ing].pop()\n", - " \n", - " act_node = ActionNode(act)\n", - " mix_node = MixNode()\n", - " mix_node.add_child(ingredient_nodes[main_ing])\n", - " act_node.add_child(mix_node)\n", - " ingredient_nodes[main_ing] = act_node\n", - " \n", - " unprocessed_ings.remove(main_ing)\n", - " \n", - " for ing in unprocessed_ings.copy():\n", - " if does_action_match(ing, act):\n", - " mix_node.add_child(ingredient_nodes[ing])\n", - " ingredient_nodes[ing] = act_node\n", - " unprocessed_ings.remove(ing)\n", - " if ing in unprocessed_main_ings:\n", - " unprocessed_main_ings.remove(ing)\n", - " \n", - " if len(mix_node.childs()) == 1:\n", - " mix_node.remove()\n", - "\n", - "# now make the same with all remaining ingredients:\n", - "while len(unprocessed_ings) > 0:\n", - " current_ing = unprocessed_ings.pop() \n", - " \n", - " # random action for that ing:\n", - " act = actions_for_ing[current_ing].pop()\n", - " \n", - " act_node = ActionNode(act)\n", - " mix_node = MixNode()\n", - " mix_node.add_child(ingredient_nodes[current_ing])\n", - " act_node.add_child(mix_node)\n", - " \n", - " ingredient_nodes[current_ing] = act_node\n", - " \n", - " \n", - " for ing in unprocessed_ings.copy():\n", - " if does_action_match(ing, act):\n", - " mix_node.add_child(ingredient_nodes[ing])\n", - " ingredient_nodes[ing] = act_node\n", - " unprocessed_ings.remove(ing)\n", - " \n", - " if len(mix_node.childs()) == 1:\n", - " mix_node.remove()\n", - "\n", - "\n", - "root_layer = set([n.root() for n in ingredient_nodes.values()])\n", - "\n", - "root_layer_without_parents = []\n", - "for node in root_layer:\n", - " if node.parent() is None:\n", - " root_layer_without_parents.append(node)\n", - "\n", - "if len(root_layer_without_parents) == 1:\n", - " root_node = root_layer_without_parents[0]\n", - "\n", - "else:\n", - " root_node = MixNode()\n", - " for r in root_layer_without_parents:\n", - " root_node.add_child(r)\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [ - { - "data": { - "image/svg+xml": [ - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "%3\n", - "\n", - "\n", - "\n", - "10\n", - "\n", - " \n", - "mix\n", - "node score: 0.0000\n", - "\n", - "\n", - "\n", - "8\n", - "\n", - " \n", - "bake\n", - "node score: 1.0000\n", - "\n", - "\n", - "\n", - "10->8\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "3\n", - "\n", - " \n", - "cut\n", - "node score: 0.2505\n", - "\n", - "\n", - "\n", - "10->3\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "6\n", - "\n", - " \n", - "fry\n", - "node score: 0.0859\n", - "\n", - "\n", - "\n", - "10->6\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "5\n", - "\n", - " \n", - "salt\n", - "node score:1.0000\n", - "\n", - "\n", - "\n", - "8->5\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "2\n", - "\n", - " \n", - "tomato\n", - "node score:1.0000\n", - "\n", - "\n", - "\n", - "3->2\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "7\n", - "\n", - " \n", - "mix\n", - "node score: 1.0000\n", - "\n", - "\n", - "\n", - "6->7\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "1\n", - "\n", - " \n", - "slice\n", - "node score: 0.1501\n", - "\n", - "\n", - "\n", - "7->1\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "4\n", - "\n", - " \n", - "rice\n", - "node score:1.0000\n", - "\n", - "\n", - "\n", - "7->4\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "0\n", - "\n", - " \n", - "onion\n", - "node score:1.0000\n", - "\n", - "\n", - "\n", - "1->0\n", - "\n", - "\n", - "\n", - "\n", - "\n" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 26, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "root_node.dot()" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [], - "source": [ - "constant_ingredients = [\"noodle\", \"onion\", \"tomato\"]\n", - "main_ingredients = ['noodle']\n", - "min_additional = 3\n", - "max_additional = 6\n", - "top_ings =3" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['noodle',\n", - " 'onion',\n", - " 'tomato',\n", - " 'salt',\n", - " 'cheese',\n", - " 'garlic clove',\n", - " 'olive oil',\n", - " 'mozzarella cheese']" - ] - }, - "execution_count": 36, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "seen_items = set(constant_ingredients)\n", - "\n", - "items = []\n", - "scores = []\n", - "\n", - "assert set(main_ingredients).issubset(set(constant_ingredients))\n", - "\n", - "# additional ingredients are choosen w.r.t all given ingredients\n", - "n_additional_ings = np.random.randint(min_additional, max_additional + 1)\n", - "\n", - "# extra ings are ingredients choosen specially for the main ingredient\n", - "n_extra_ings = int((len(main_ingredients) / len(constant_ingredients)) * n_additional_ings)\n", - "\n", - "if n_extra_ings > n_additional_ings:\n", - " n_extra_ings = n_additional_ings\n", - "\n", - " \n", - "# choose extra ingredients\n", - "extra_candidates = []\n", - "extra_weights = []\n", - "\n", - "for ing in main_ingredients:\n", - " candidates, weights = normalized_score(ing, m_base_mix)\n", - " extra_candidates.append(candidates[:10])\n", - " extra_weights.append(weights[:10])\n", - "\n", - "extra_ingredients = ea_tools.combined_wheel_of_fortune_selection(extra_candidates,\n", - " extra_weights,\n", - " n_extra_ings)\n", - "\n", - "for ing in constant_ingredients:\n", - " # find best matching ingredients\n", - " best_items = []\n", - " best_scores = []\n", - "\n", - " candidates, weights = m_base_mix.get_adjacent(ing)\n", - " i = 0\n", - " while i < len(candidates) and len(best_items) < top_ings:\n", - " if candidates[i] not in seen_items:\n", - " best_items.append(candidates[i])\n", - " best_scores.append(weights[i])\n", - " i += 1\n", - "\n", - " items.append(best_items)\n", - " scores.append(best_scores)\n", - "\n", - "#TODO: error handling if too few options are availabale!\n", - "\n", - "additional_ingredients = ea_tools.combined_wheel_of_fortune_selection(items,\n", - " scores,\n", - " n_additional_ings - n_extra_ings)\n", - "list(constant_ingredients) + list(additional_ingredients) + list(extra_ingredients)" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(array(['ricotta cheese', 'spaghetti sauce', 'mozzarella cheese', 'cheese',\n", - " 'spinach', 'sausage', 'ground beef', 'tomato sauce', 'onion',\n", - " 'basil', 'broccoli', 'parsley', 'mushroom soup', 'mushroom',\n", - " 'sauce', 'egg', 'garlic clove', 'chicken', 'milk', 'water', 'salt',\n", - " 'zucchini', 'tomato', 'pepper', 'seasoning', 'green pepper',\n", - " 'shrimp', 'soy sauce', 'butter', 'red pepper', 'oregano',\n", - " 'clove garlic', 'olive oil', 'pork', 'carrot', 'green onion',\n", - " 'cream cheese', 'garlic', 'chicken broth', 'tablespoon butter',\n", - " 'red bell pepper', 'flour', 'cream', 'black pepper',\n", - " 'vegetable oil', 'chicken breast', 'sugar'], dtype='\n", "\n", - "\n", - "\n", + "\n", + "\n", "%3\n", - "\n", - "\n", + "\n", + "\n", "\n", - "9\n", - "\n", - " \n", - "mix\n", - "node score: 0.1429\n", + "146\n", + "\n", + " \n", + "mix\n", + "node score: 0.0000\n", "\n", - "\n", + "\n", "\n", - "5\n", - "\n", - " \n", - "bake\n", - "node score: 1.0000\n", + "144\n", + "\n", + " \n", + "bake\n", + "node score: 1.0000\n", "\n", - "\n", + "\n", "\n", - "9->5\n", - "\n", - "\n", + "146->144\n", + "\n", + "\n", "\n", - "\n", - "\n", - "7\n", - "\n", - " \n", - "cook\n", - "node score: 0.9182\n", - "\n", - "\n", - "\n", - "9->7\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "8\n", - "\n", - " \n", - "beat\n", - "node score: 0.7344\n", - "\n", - "\n", - "\n", - "9->8\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "3\n", - "\n", - " \n", - "salt\n", - "node score:1.0000\n", - "\n", - "\n", - "\n", - "5->3\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "6\n", - "\n", - " \n", - "mix\n", - "node score: 0.6667\n", - "\n", - "\n", - "\n", - "7->6\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "0\n", - "\n", - " \n", - "bacon\n", - "node score:1.0000\n", - "\n", - "\n", - "\n", - "6->0\n", - "\n", - "\n", - "\n", - "\n", + "\n", "\n", - "2\n", - "\n", - " \n", - "onion\n", - "node score:1.0000\n", + "142\n", + "\n", + " \n", + "boil\n", + "node score: 1.0000\n", "\n", - "\n", + "\n", "\n", - "6->2\n", - "\n", - "\n", + "146->142\n", + "\n", + "\n", "\n", - "\n", - "\n", - "1\n", - "\n", - " \n", - "tomato\n", - "node score:1.0000\n", - "\n", - "\n", - "\n", - "6->1\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "4\n", - "\n", - " \n", - "egg\n", - "node score:1.0000\n", - "\n", - "\n", - "\n", - "8->4\n", - "\n", - "\n", - "\n", - "\n", - "\n" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/svg+xml": [ - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "%3\n", - "\n", - "\n", - "\n", - "25\n", - "\n", - " \n", - "mix\n", - "node score: 0.1000\n", - "\n", - "\n", - "\n", - "24\n", - "\n", - " \n", - "heat\n", - "node score: 1.0000\n", - "\n", - "\n", - "\n", - "25->24\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "20\n", - "\n", - " \n", - "cook\n", - "node score: 0.7815\n", - "\n", - "\n", - "\n", - "25->20\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "22\n", - "\n", - " \n", - "bake\n", - "node score: 1.0000\n", - "\n", - "\n", - "\n", - "25->22\n", - "\n", - "\n", - "\n", - "\n", + "\n", "\n", - "23\n", - "\n", - " \n", - "mix\n", - "node score: 1.0000\n", + "145\n", + "\n", + " \n", + "mix\n", + "node score: 1.0000\n", "\n", - "\n", + "\n", "\n", - "24->23\n", - "\n", - "\n", + "144->145\n", + "\n", + "\n", "\n", - "\n", + "\n", "\n", - "14\n", - "\n", - " \n", - "olive oil\n", - "node score:1.0000\n", + "137\n", + "\n", + " \n", + "cheese\n", + "node score:0.6065\n", "\n", - "\n", + "\n", "\n", - "23->14\n", - "\n", - "\n", + "145->137\n", + "\n", + "\n", "\n", - "\n", + "\n", "\n", - "12\n", - "\n", - " \n", - "tomato\n", - "node score:1.0000\n", + "136\n", + "\n", + " \n", + "spaghetti sauce\n", + "node score:0.6065\n", "\n", - "\n", + "\n", "\n", - "23->12\n", - "\n", - "\n", + "145->136\n", + "\n", + "\n", "\n", - "\n", - "\n", - "19\n", - "\n", - " \n", - "mix\n", - "node score: 0.8333\n", + "\n", + "\n", + "138\n", + "\n", + " \n", + "ricotta cheese\n", + "node score:0.6065\n", "\n", - "\n", - "\n", - "20->19\n", - "\n", - "\n", + "\n", + "\n", + "145->138\n", + "\n", + "\n", "\n", - "\n", + "\n", "\n", - "13\n", - "\n", - " \n", - "onion\n", - "node score:1.0000\n", + "143\n", + "\n", + " \n", + "mix\n", + "node score: 0.3333\n", "\n", - "\n", + "\n", "\n", - "19->13\n", - "\n", - "\n", + "142->143\n", + "\n", + "\n", "\n", - "\n", + "\n", "\n", - "11\n", - "\n", - " \n", - "bacon\n", - "node score:1.0000\n", + "140\n", + "\n", + " \n", + "dice\n", + "node score: 1.0000\n", "\n", - "\n", + "\n", "\n", - "19->11\n", - "\n", - "\n", + "143->140\n", + "\n", + "\n", "\n", - "\n", - "\n", - "16\n", - "\n", - " \n", - "cheese\n", - "node score:1.0000\n", - "\n", - "\n", - "\n", - "19->16\n", - "\n", - "\n", - "\n", - "\n", + "\n", "\n", - "17\n", - "\n", - " \n", - "garlic clove\n", - "node score:1.0000\n", + "141\n", + "\n", + " \n", + "ground beef\n", + "node score:0.6065\n", "\n", - "\n", + "\n", "\n", - "19->17\n", - "\n", - "\n", + "143->141\n", + "\n", + "\n", "\n", - "\n", - "\n", - "21\n", - "\n", - " \n", - "mix\n", - "node score: 1.0000\n", + "\n", + "\n", + "135\n", + "\n", + " \n", + "noodle\n", + "node score:0.6065\n", "\n", - "\n", - "\n", - "22->21\n", - "\n", - "\n", + "\n", + "\n", + "143->135\n", + "\n", + "\n", "\n", - "\n", - "\n", - "15\n", - "\n", - " \n", - "salt\n", - "node score:1.0000\n", + "\n", + "\n", + "139\n", + "\n", + " \n", + "onion\n", + "node score:1.0000\n", "\n", - "\n", - "\n", - "21->15\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "18\n", - "\n", - " \n", - "egg\n", - "node score:1.0000\n", - "\n", - "\n", - "\n", - "21->18\n", - "\n", - "\n", + "\n", + "\n", + "140->139\n", + "\n", + "\n", "\n", "\n", "\n" ], "text/plain": [ - "" + "" ] }, + "execution_count": 47, "metadata": {}, - "output_type": "display_data" - }, + "output_type": "execute_result" + } + ], + "source": [ + "d = p.population[1].root().dot()\n", + "d" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [ + { + "data": { + "text/markdown": [ + "**Ingredients**:\n", + " * cheese\n", + " * ground beef\n", + " * onion\n", + " * spaghetti sauce\n", + " * ricotta cheese\n", + " * noodle\n", + "\n", + "\n", + "**Instructions**:\n", + "\n", + "| Step | Instruction |\n", + "| ----:|:----------- |\n", + "| 1 | Mix cheese, spaghetti sauce and ricotta cheese. Then bake it. |\n", + "| 2 | dice onion and mix it with ground beef and noodle. Then boil it. |\n", + "| 3 | Mix together the results of step 1 and step 2. |\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 48, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "p.population[1].root().to_instruction().to_markdown()" + ] + }, + { + "cell_type": "code", + "execution_count": 98, + "metadata": {}, + "outputs": [ { "data": { "image/svg+xml": [ @@ -2349,554 +2129,206 @@ "\n", "\n", - "\n", - "\n", + "\n", + "\n", "%3\n", - "\n", - "\n", + "\n", + "\n", "\n", - "32\n", - "\n", - " \n", - "heat\n", - "node score: 0.8099\n", + "2513\n", + "\n", + " \n", + "mix\n", + "node score: 0.2000\n", "\n", - "\n", + "\n", "\n", - "31\n", - "\n", - " \n", - "mix\n", - "node score: 0.8333\n", + "2507\n", + "\n", + " \n", + "cheese\n", + "node score:0.0000\n", "\n", - "\n", + "\n", "\n", - "32->31\n", - "\n", - "\n", + "2513->2507\n", + "\n", + "\n", "\n", - "\n", + "\n", "\n", - "27\n", - "\n", - " \n", - "bacon\n", - "node score:1.0000\n", + "2506\n", + "\n", + " \n", + "noodle\n", + "node score:0.0000\n", "\n", - "\n", + "\n", "\n", - "31->27\n", - "\n", - "\n", + "2513->2506\n", + "\n", + "\n", "\n", - "\n", + "\n", "\n", - "28\n", - "\n", - " \n", - "tomato\n", - "node score:1.0000\n", + "2511\n", + "\n", + " \n", + "heat\n", + "node score: 1.0000\n", "\n", - "\n", + "\n", "\n", - "31->28\n", - "\n", - "\n", + "2513->2511\n", + "\n", + "\n", "\n", - "\n", + "\n", + "\n", + "2503\n", + "\n", + " \n", + "chop\n", + "node score: 1.0000\n", + "\n", + "\n", + "\n", + "2513->2503\n", + "\n", + "\n", + "\n", + "\n", "\n", - "29\n", - "\n", - " \n", - "onion\n", - "node score:1.0000\n", + "2512\n", + "\n", + " \n", + "mix\n", + "node score: 0.3333\n", "\n", - "\n", + "\n", "\n", - "31->29\n", - "\n", - "\n", + "2511->2512\n", + "\n", + "\n", "\n", - "\n", + "\n", "\n", - "30\n", - "\n", - " \n", - "salt\n", - "node score:1.0000\n", - "\n", - "\n", - "\n", - "31->30\n", - "\n", - "\n", - "\n", - "\n", - "\n" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/svg+xml": [ - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "%3\n", - "\n", - "\n", - "\n", - "40\n", - "\n", - " \n", - "mix\n", - "node score: 0.0000\n", - "\n", - "\n", - "\n", - "38\n", + "2505\n", "\n", " \n", - "heat\n", - "node score: 0.9679\n", + "slice\n", + "node score: 1.0000\n", "\n", - "\n", - "\n", - "40->38\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "39\n", - "\n", - " \n", - "place\n", - "node score: 0.3942\n", - "\n", - "\n", - "\n", - "40->39\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "37\n", - "\n", - " \n", - "simmer\n", - "node score: 0.7959\n", - "\n", - "\n", + "\n", "\n", - "40->37\n", - "\n", - "\n", + "2512->2505\n", + "\n", + "\n", "\n", - "\n", - "\n", - "36\n", + "\n", + "\n", + "2501\n", + "\n", + " \n", + "chop\n", + "node score: 1.0000\n", + "\n", + "\n", + "\n", + "2512->2501\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "2508\n", + "\n", + " \n", + "olive oil\n", + "node score:0.6065\n", + "\n", + "\n", + "\n", + "2512->2508\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "2510\n", + "\n", + " \n", + "chop\n", + "node score: 1.0000\n", + "\n", + "\n", + "\n", + "2512->2510\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "2504\n", "\n", " \n", "onion\n", "node score:1.0000\n", "\n", - "\n", - "\n", - "38->36\n", + "\n", + "\n", + "2505->2504\n", "\n", "\n", "\n", - "\n", - "\n", - "34\n", + "\n", + "\n", + "2500\n", "\n", " \n", "bacon\n", "node score:1.0000\n", "\n", - "\n", - "\n", - "39->34\n", + "\n", + "\n", + "2501->2500\n", "\n", "\n", "\n", - "\n", - "\n", - "35\n", - "\n", - " \n", - "tomato\n", - "node score:1.0000\n", - "\n", - "\n", - "\n", - "37->35\n", - "\n", - "\n", - "\n", - "\n", - "\n" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/svg+xml": [ - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "%3\n", - "\n", - "\n", - "\n", - "48\n", - "\n", - " \n", - "mix\n", - "node score: 0.0000\n", - "\n", - "\n", - "\n", - "46\n", - "\n", - " \n", - "heat\n", - "node score: 0.8198\n", - "\n", - "\n", - "\n", - "48->46\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "47\n", - "\n", - " \n", - "simmer\n", - "node score: 0.7959\n", - "\n", - "\n", - "\n", - "48->47\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "45\n", - "\n", - " \n", - "mix\n", - "node score: 1.0000\n", - "\n", - "\n", - "\n", - "46->45\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "42\n", - "\n", - " \n", - "bacon\n", - "node score:1.0000\n", - "\n", - "\n", - "\n", - "45->42\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "44\n", - "\n", - " \n", - "onion\n", - "node score:1.0000\n", - "\n", - "\n", - "\n", - "45->44\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "43\n", - "\n", - " \n", - "tomato\n", - "node score:1.0000\n", - "\n", - "\n", - "\n", - "47->43\n", - "\n", - "\n", - "\n", - "\n", - "\n" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/svg+xml": [ - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "%3\n", - "\n", - "\n", - "\n", - "64\n", - "\n", - " \n", - "mix\n", - "node score: 0.0000\n", - "\n", - "\n", - "\n", - "59\n", - "\n", - " \n", - "beat\n", - "node score: 0.7344\n", - "\n", - "\n", - "\n", - "64->59\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "63\n", - "\n", - " \n", - "bake\n", - "node score: 0.4789\n", - "\n", - "\n", - "\n", - "64->63\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "54\n", - "\n", - " \n", - "egg\n", - "node score:1.0000\n", - "\n", - "\n", - "\n", - "59->54\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "62\n", - "\n", - " \n", - "mix\n", - "node score: 0.2000\n", - "\n", - "\n", - "\n", - "63->62\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "61\n", - "\n", - " \n", - "cook\n", - "node score: 0.7853\n", - "\n", - "\n", - "\n", - "62->61\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "55\n", - "\n", - " \n", - "cheese\n", - "node score:1.0000\n", - "\n", - "\n", - "\n", - "62->55\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "60\n", - "\n", - " \n", - "mix\n", - "node score: 0.4286\n", - "\n", - "\n", - "\n", - "61->60\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "52\n", - "\n", - " \n", - "onion\n", - "node score:1.0000\n", - "\n", - "\n", - "\n", - "60->52\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "58\n", - "\n", - " \n", - "heat\n", - "node score: 1.0000\n", - "\n", - "\n", - "\n", - "60->58\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "50\n", - "\n", - " \n", - "bacon\n", - "node score:1.0000\n", - "\n", - "\n", - "\n", - "60->50\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "57\n", - "\n", - " \n", - "mix\n", - "node score: 1.0000\n", - "\n", - "\n", - "\n", - "58->57\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "56\n", - "\n", - " \n", - "olive oil\n", - "node score:1.0000\n", - "\n", - "\n", - "\n", - "57->56\n", - "\n", - "\n", - "\n", - "\n", + "\n", "\n", - "51\n", - "\n", - " \n", - "tomato\n", - "node score:1.0000\n", + "2509\n", + "\n", + " \n", + "salt\n", + "node score:0.0000\n", "\n", - "\n", + "\n", "\n", - "57->51\n", - "\n", - "\n", + "2510->2509\n", + "\n", + "\n", "\n", - "\n", - "\n", - "53\n", - "\n", - " \n", - "garlic clove\n", - "node score:1.0000\n", + "\n", + "\n", + "2502\n", + "\n", + " \n", + "tomato\n", + "node score:0.6065\n", "\n", - "\n", - "\n", - "57->53\n", - "\n", - "\n", + "\n", + "\n", + "2503->2502\n", + "\n", + "\n", "\n", "\n", "\n" ], "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -2911,466 +2343,178 @@ "\n", "\n", - "\n", + "\n", "\n", "%3\n", - "\n", - "\n", + "\n", + "\n", "\n", - "74\n", - "\n", - " \n", - "cook\n", - "node score: 0.7125\n", + "2526\n", + "\n", + " \n", + "mix\n", + "node score: 0.8333\n", "\n", - "\n", + "\n", "\n", - "73\n", - "\n", - " \n", - "mix\n", - "node score: 0.2500\n", + "2518\n", + "\n", + " \n", + "chop\n", + "node score: 1.0000\n", "\n", - "\n", + "\n", "\n", - "74->73\n", - "\n", - "\n", + "2526->2518\n", + "\n", + "\n", "\n", - "\n", - "\n", - "66\n", - "\n", - " \n", - "bacon\n", - "node score:1.0000\n", - "\n", - "\n", - "\n", - "73->66\n", - "\n", - "\n", - "\n", - "\n", + "\n", "\n", - "72\n", - "\n", - " \n", - "heat\n", - "node score: 0.8920\n", + "2522\n", + "\n", + " \n", + "salt\n", + "node score:0.1353\n", "\n", - "\n", + "\n", "\n", - "73->72\n", - "\n", - "\n", + "2526->2522\n", + "\n", + "\n", "\n", - "\n", + "\n", "\n", - "71\n", - "\n", - " \n", - "mix\n", - "node score: 1.0000\n", + "2524\n", + "\n", + " \n", + "cook\n", + "node score: 1.0000\n", "\n", - "\n", + "\n", "\n", - "72->71\n", - "\n", - "\n", + "2526->2524\n", + "\n", + "\n", "\n", - "\n", - "\n", - "68\n", - "\n", - " \n", - "onion\n", - "node score:1.0000\n", - "\n", - "\n", - "\n", - "71->68\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "69\n", - "\n", - " \n", - "salt\n", - "node score:1.0000\n", - "\n", - "\n", - "\n", - "71->69\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "70\n", - "\n", - " \n", - "olive oil\n", - "node score:1.0000\n", - "\n", - "\n", - "\n", - "71->70\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "67\n", - "\n", - " \n", - "tomato\n", - "node score:1.0000\n", - "\n", - "\n", - "\n", - "71->67\n", - "\n", - "\n", - "\n", - "\n", - "\n" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/svg+xml": [ - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "%3\n", - "\n", - "\n", - "\n", - "82\n", - "\n", - " \n", - "mix\n", - "node score: 0.0000\n", - "\n", - "\n", - "\n", - "81\n", - "\n", - " \n", - "heat\n", - "node score: 1.0000\n", - "\n", - "\n", - "\n", - "82->81\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "80\n", - "\n", - " \n", - "cook\n", - "node score: 1.0000\n", - "\n", - "\n", - "\n", - "82->80\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "77\n", - "\n", - " \n", - "tomato\n", - "node score:1.0000\n", - "\n", - "\n", - "\n", - "81->77\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "79\n", - "\n", - " \n", - "mix\n", - "node score: 1.0000\n", - "\n", - "\n", - "\n", - "80->79\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "76\n", - "\n", - " \n", - "bacon\n", - "node score:1.0000\n", - "\n", - "\n", - "\n", - "79->76\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "78\n", - "\n", - " \n", - "onion\n", - "node score:1.0000\n", - "\n", - "\n", - "\n", - "79->78\n", - "\n", - "\n", - "\n", - "\n", - "\n" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/svg+xml": [ - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "%3\n", - "\n", - "\n", - "\n", - "97\n", - "\n", - " \n", - "mix\n", - "node score: 0.0000\n", - "\n", - "\n", - "\n", - "96\n", - "\n", - " \n", - "heat\n", - "node score: 0.9279\n", - "\n", - "\n", - "\n", - "97->96\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "94\n", - "\n", - " \n", - "cool\n", - "node score: 0.6073\n", - "\n", - "\n", - "\n", - "97->94\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "95\n", - "\n", - " \n", - "mix\n", - "node score: 0.5000\n", - "\n", - "\n", - "\n", - "96->95\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "92\n", - "\n", - " \n", - "cook\n", - "node score: 0.7929\n", - "\n", - "\n", - "\n", - "95->92\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "85\n", - "\n", - " \n", - "tomato\n", - "node score:1.0000\n", - "\n", - "\n", - "\n", - "95->85\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "91\n", - "\n", - " \n", - "mix\n", - "node score: 0.8333\n", - "\n", - "\n", - "\n", - "92->91\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "84\n", - "\n", - " \n", - "bacon\n", - "node score:1.0000\n", - "\n", - "\n", - "\n", - "91->84\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "86\n", - "\n", - " \n", - "onion\n", - "node score:1.0000\n", - "\n", - "\n", - "\n", - "91->86\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "88\n", - "\n", - " \n", - "olive oil\n", - "node score:1.0000\n", - "\n", - "\n", - "\n", - "91->88\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "89\n", - "\n", - " \n", - "garlic clove\n", - "node score:1.0000\n", - "\n", - "\n", - "\n", - "91->89\n", - "\n", - "\n", - "\n", - "\n", + "\n", "\n", - "93\n", - "\n", - " \n", - "mix\n", - "node score: 1.0000\n", + "2523\n", + "\n", + " \n", + "cheese\n", + "node score:0.0000\n", "\n", - "\n", + "\n", "\n", - "94->93\n", - "\n", - "\n", + "2526->2523\n", + "\n", + "\n", "\n", - "\n", - "\n", - "87\n", - "\n", - " \n", - "salt\n", - "node score:1.0000\n", + "\n", + "\n", + "2517\n", + "\n", + " \n", + "tomato\n", + "node score:0.6065\n", "\n", - "\n", - "\n", - "93->87\n", - "\n", - "\n", + "\n", + "\n", + "2518->2517\n", + "\n", + "\n", "\n", - "\n", - "\n", - "90\n", - "\n", - " \n", - "egg\n", - "node score:1.0000\n", + "\n", + "\n", + "2525\n", + "\n", + " \n", + "mix\n", + "node score: 0.3333\n", "\n", - "\n", - "\n", - "93->90\n", - "\n", - "\n", + "\n", + "\n", + "2524->2525\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "2520\n", + "\n", + " \n", + "chop\n", + "node score: 1.0000\n", + "\n", + "\n", + "\n", + "2525->2520\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "2521\n", + "\n", + " \n", + "noodle\n", + "node score:0.6065\n", + "\n", + "\n", + "\n", + "2525->2521\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "2516\n", + "\n", + " \n", + "slice\n", + "node score: 1.0000\n", + "\n", + "\n", + "\n", + "2525->2516\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "2519\n", + "\n", + " \n", + "onion\n", + "node score:1.0000\n", + "\n", + "\n", + "\n", + "2520->2519\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "2515\n", + "\n", + " \n", + "bacon\n", + "node score:1.0000\n", + "\n", + "\n", + "\n", + "2516->2515\n", + "\n", + "\n", "\n", "\n", "\n" ], "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -3385,150 +2529,1718 @@ "\n", "\n", - "\n", - "\n", + "\n", + "\n", "%3\n", - "\n", - "\n", + "\n", + "\n", "\n", - "108\n", - "\n", - " \n", - "mix\n", - "node score: 0.0000\n", + "2547\n", + "\n", + " \n", + "mix\n", + "node score: 0.0000\n", "\n", - "\n", + "\n", "\n", - "107\n", - "\n", - " \n", - "cook\n", - "node score: 0.9182\n", + "2545\n", + "\n", + " \n", + "bake\n", + "node score: 0.8889\n", "\n", - "\n", + "\n", "\n", - "108->107\n", - "\n", - "\n", + "2547->2545\n", + "\n", + "\n", "\n", - "\n", - "\n", - "103\n", - "\n", - " \n", - "cool\n", - "node score: 0.5490\n", + "\n", + "\n", + "2542\n", + "\n", + " \n", + "brush\n", + "node score: 1.0000\n", "\n", - "\n", - "\n", - "108->103\n", - "\n", - "\n", + "\n", + "\n", + "2547->2542\n", + "\n", + "\n", "\n", - "\n", + "\n", "\n", - "106\n", - "\n", - " \n", - "mix\n", - "node score: 0.0000\n", + "2546\n", + "\n", + " \n", + "mix\n", + "node score: 0.3611\n", "\n", - "\n", + "\n", "\n", - "107->106\n", - "\n", - "\n", + "2545->2546\n", + "\n", + "\n", "\n", - "\n", + "\n", "\n", - "99\n", - "\n", - " \n", - "bacon\n", - "node score:1.0000\n", + "2534\n", + "\n", + " \n", + "noodle\n", + "node score:0.6065\n", "\n", - "\n", + "\n", "\n", - "106->99\n", - "\n", - "\n", + "2546->2534\n", + "\n", + "\n", "\n", - "\n", + "\n", "\n", - "105\n", - "\n", - " \n", - "simmer\n", - "node score: 0.6840\n", + "2538\n", + "\n", + " \n", + "beat\n", + "node score: 1.0000\n", "\n", - "\n", + "\n", "\n", - "106->105\n", - "\n", - "\n", + "2546->2538\n", + "\n", + "\n", "\n", - "\n", - "\n", - "104\n", - "\n", - " \n", - "mix\n", - "node score: 1.0000\n", - "\n", - "\n", - "\n", - "105->104\n", - "\n", - "\n", - "\n", - "\n", + "\n", "\n", - "100\n", - "\n", - " \n", - "tomato\n", - "node score:1.0000\n", + "2531\n", + "\n", + " \n", + "slice\n", + "node score: 1.0000\n", "\n", - "\n", + "\n", "\n", - "104->100\n", - "\n", - "\n", + "2546->2531\n", + "\n", + "\n", "\n", - "\n", - "\n", - "101\n", - "\n", - " \n", - "onion\n", - "node score:1.0000\n", + "\n", + "\n", + "2536\n", + "\n", + " \n", + "salt\n", + "node score:0.0000\n", "\n", - "\n", - "\n", - "104->101\n", - "\n", - "\n", + "\n", + "\n", + "2546->2536\n", + "\n", + "\n", "\n", - "\n", + "\n", "\n", - "102\n", - "\n", - " \n", - "salt\n", - "node score:1.0000\n", + "2533\n", + "\n", + " \n", + "chop\n", + "node score: 1.0000\n", "\n", - "\n", + "\n", "\n", - "103->102\n", - "\n", - "\n", + "2546->2533\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "2544\n", + "\n", + " \n", + "chop\n", + "node score: 1.0000\n", + "\n", + "\n", + "\n", + "2546->2544\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "2540\n", + "\n", + " \n", + "cut\n", + "node score: 1.0000\n", + "\n", + "\n", + "\n", + "2546->2540\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "2535\n", + "\n", + " \n", + "olive oil\n", + "node score:0.6065\n", + "\n", + "\n", + "\n", + "2546->2535\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "2529\n", + "\n", + " \n", + "chop\n", + "node score: 1.0000\n", + "\n", + "\n", + "\n", + "2546->2529\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "2537\n", + "\n", + " \n", + "cheese\n", + "node score:1.0000\n", + "\n", + "\n", + "\n", + "2538->2537\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "2530\n", + "\n", + " \n", + "tomato\n", + "node score:1.0000\n", + "\n", + "\n", + "\n", + "2531->2530\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "2532\n", + "\n", + " \n", + "onion\n", + "node score:1.0000\n", + "\n", + "\n", + "\n", + "2533->2532\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "2543\n", + "\n", + " \n", + "spinach\n", + "node score:1.0000\n", + "\n", + "\n", + "\n", + "2544->2543\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "2539\n", + "\n", + " \n", + "garlic clove\n", + "node score:1.0000\n", + "\n", + "\n", + "\n", + "2540->2539\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "2528\n", + "\n", + " \n", + "bacon\n", + "node score:1.0000\n", + "\n", + "\n", + "\n", + "2529->2528\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "2541\n", + "\n", + " \n", + "egg\n", + "node score:0.6065\n", + "\n", + "\n", + "\n", + "2542->2541\n", + "\n", + "\n", "\n", "\n", "\n" ], "text/plain": [ - "" + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "%3\n", + "\n", + "\n", + "\n", + "2558\n", + "\n", + " \n", + "mix\n", + "node score: 0.0000\n", + "\n", + "\n", + "\n", + "2556\n", + "\n", + " \n", + "simmer\n", + "node score: 0.3333\n", + "\n", + "\n", + "\n", + "2558->2556\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "2552\n", + "\n", + " \n", + "cut\n", + "node score: 1.0000\n", + "\n", + "\n", + "\n", + "2558->2552\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "2557\n", + "\n", + " \n", + "mix\n", + "node score: 0.3333\n", + "\n", + "\n", + "\n", + "2556->2557\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "2555\n", + "\n", + " \n", + "noodle\n", + "node score:0.6065\n", + "\n", + "\n", + "\n", + "2557->2555\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "2554\n", + "\n", + " \n", + "chop\n", + "node score: 1.0000\n", + "\n", + "\n", + "\n", + "2557->2554\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "2550\n", + "\n", + " \n", + "chop\n", + "node score: 1.0000\n", + "\n", + "\n", + "\n", + "2557->2550\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "2553\n", + "\n", + " \n", + "onion\n", + "node score:1.0000\n", + "\n", + "\n", + "\n", + "2554->2553\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "2549\n", + "\n", + " \n", + "bacon\n", + "node score:1.0000\n", + "\n", + "\n", + "\n", + "2550->2549\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "2551\n", + "\n", + " \n", + "tomato\n", + "node score:0.6065\n", + "\n", + "\n", + "\n", + "2552->2551\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "%3\n", + "\n", + "\n", + "\n", + "2570\n", + "\n", + " \n", + "mix\n", + "node score: 0.1111\n", + "\n", + "\n", + "\n", + "2567\n", + "\n", + " \n", + "salt\n", + "node score:0.1353\n", + "\n", + "\n", + "\n", + "2570->2567\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "2563\n", + "\n", + " \n", + "chop\n", + "node score: 1.0000\n", + "\n", + "\n", + "\n", + "2570->2563\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "2568\n", + "\n", + " \n", + "bake\n", + "node score: 1.0000\n", + "\n", + "\n", + "\n", + "2570->2568\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "2561\n", + "\n", + " \n", + "cut\n", + "node score: 1.0000\n", + "\n", + "\n", + "\n", + "2570->2561\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "2562\n", + "\n", + " \n", + "tomato\n", + "node score:0.6065\n", + "\n", + "\n", + "\n", + "2563->2562\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "2569\n", + "\n", + " \n", + "mix\n", + "node score: 0.0000\n", + "\n", + "\n", + "\n", + "2568->2569\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "2566\n", + "\n", + " \n", + "noodle\n", + "node score:0.6065\n", + "\n", + "\n", + "\n", + "2569->2566\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "2565\n", + "\n", + " \n", + "chop\n", + "node score: 1.0000\n", + "\n", + "\n", + "\n", + "2569->2565\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "2564\n", + "\n", + " \n", + "onion\n", + "node score:1.0000\n", + "\n", + "\n", + "\n", + "2565->2564\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "2560\n", + "\n", + " \n", + "bacon\n", + "node score:0.6065\n", + "\n", + "\n", + "\n", + "2561->2560\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "%3\n", + "\n", + "\n", + "\n", + "2586\n", + "\n", + " \n", + "mix\n", + "node score: 0.0000\n", + "\n", + "\n", + "\n", + "2582\n", + "\n", + " \n", + "boil\n", + "node score: 0.8000\n", + "\n", + "\n", + "\n", + "2586->2582\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "2584\n", + "\n", + " \n", + "cook\n", + "node score: 1.0000\n", + "\n", + "\n", + "\n", + "2586->2584\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "2575\n", + "\n", + " \n", + "chop\n", + "node score: 1.0000\n", + "\n", + "\n", + "\n", + "2586->2575\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "2583\n", + "\n", + " \n", + "mix\n", + "node score: 0.4000\n", + "\n", + "\n", + "\n", + "2582->2583\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "2573\n", + "\n", + " \n", + "chop\n", + "node score: 1.0000\n", + "\n", + "\n", + "\n", + "2583->2573\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "2578\n", + "\n", + " \n", + "noodle\n", + "node score:0.6065\n", + "\n", + "\n", + "\n", + "2583->2578\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "2580\n", + "\n", + " \n", + "olive oil\n", + "node score:0.6065\n", + "\n", + "\n", + "\n", + "2583->2580\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "2579\n", + "\n", + " \n", + "salt\n", + "node score:0.0000\n", + "\n", + "\n", + "\n", + "2583->2579\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "2577\n", + "\n", + " \n", + "slice\n", + "node score: 1.0000\n", + "\n", + "\n", + "\n", + "2583->2577\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "2572\n", + "\n", + " \n", + "bacon\n", + "node score:1.0000\n", + "\n", + "\n", + "\n", + "2573->2572\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "2576\n", + "\n", + " \n", + "onion\n", + "node score:1.0000\n", + "\n", + "\n", + "\n", + "2577->2576\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "2581\n", + "\n", + " \n", + "cheese\n", + "node score:0.6065\n", + "\n", + "\n", + "\n", + "2584->2581\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "2574\n", + "\n", + " \n", + "tomato\n", + "node score:0.6065\n", + "\n", + "\n", + "\n", + "2575->2574\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "%3\n", + "\n", + "\n", + "\n", + "2604\n", + "\n", + " \n", + "mix\n", + "node score: 0.0000\n", + "\n", + "\n", + "\n", + "2589\n", + "\n", + " \n", + "slice\n", + "node score: 1.0000\n", + "\n", + "\n", + "\n", + "2604->2589\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "2591\n", + "\n", + " \n", + "chop\n", + "node score: 1.0000\n", + "\n", + "\n", + "\n", + "2604->2591\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "2602\n", + "\n", + " \n", + "bake\n", + "node score: 1.0000\n", + "\n", + "\n", + "\n", + "2604->2602\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "2588\n", + "\n", + " \n", + "bacon\n", + "node score:0.6065\n", + "\n", + "\n", + "\n", + "2589->2588\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "2590\n", + "\n", + " \n", + "tomato\n", + "node score:0.6065\n", + "\n", + "\n", + "\n", + "2591->2590\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "2603\n", + "\n", + " \n", + "mix\n", + "node score: 0.4286\n", + "\n", + "\n", + "\n", + "2602->2603\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "2600\n", + "\n", + " \n", + "olive oil\n", + "node score:0.6065\n", + "\n", + "\n", + "\n", + "2603->2600\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "2599\n", + "\n", + " \n", + "peel\n", + "node score: 1.0000\n", + "\n", + "\n", + "\n", + "2603->2599\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "2597\n", + "\n", + " \n", + "salt\n", + "node score:0.0000\n", + "\n", + "\n", + "\n", + "2603->2597\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "2601\n", + "\n", + " \n", + "tomato sauce\n", + "node score:0.0000\n", + "\n", + "\n", + "\n", + "2603->2601\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "2593\n", + "\n", + " \n", + "dice\n", + "node score: 1.0000\n", + "\n", + "\n", + "\n", + "2603->2593\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "2596\n", + "\n", + " \n", + "chop\n", + "node score: 1.0000\n", + "\n", + "\n", + "\n", + "2603->2596\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "2594\n", + "\n", + " \n", + "noodle\n", + "node score:0.6065\n", + "\n", + "\n", + "\n", + "2603->2594\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "2598\n", + "\n", + " \n", + "garlic clove\n", + "node score:1.0000\n", + "\n", + "\n", + "\n", + "2599->2598\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "2592\n", + "\n", + " \n", + "onion\n", + "node score:1.0000\n", + "\n", + "\n", + "\n", + "2593->2592\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "2595\n", + "\n", + " \n", + "cheese\n", + "node score:1.0000\n", + "\n", + "\n", + "\n", + "2596->2595\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "%3\n", + "\n", + "\n", + "\n", + "2620\n", + "\n", + " \n", + "mix\n", + "node score: 0.0667\n", + "\n", + "\n", + "\n", + "2618\n", + "\n", + " \n", + "simmer\n", + "node score: 0.5000\n", + "\n", + "\n", + "\n", + "2620->2618\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "2609\n", + "\n", + " \n", + "chop\n", + "node score: 1.0000\n", + "\n", + "\n", + "\n", + "2620->2609\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "2617\n", + "\n", + " \n", + "cheese\n", + "node score:0.0000\n", + "\n", + "\n", + "\n", + "2620->2617\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "2616\n", + "\n", + " \n", + "crack\n", + "node score: 1.0000\n", + "\n", + "\n", + "\n", + "2620->2616\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "2619\n", + "\n", + " \n", + "mix\n", + "node score: 0.3333\n", + "\n", + "\n", + "\n", + "2618->2619\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "2607\n", + "\n", + " \n", + "chop\n", + "node score: 1.0000\n", + "\n", + "\n", + "\n", + "2619->2607\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "2611\n", + "\n", + " \n", + "cut\n", + "node score: 1.0000\n", + "\n", + "\n", + "\n", + "2619->2611\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "2612\n", + "\n", + " \n", + "noodle\n", + "node score:0.6065\n", + "\n", + "\n", + "\n", + "2619->2612\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "2614\n", + "\n", + " \n", + "salt\n", + "node score:0.0000\n", + "\n", + "\n", + "\n", + "2619->2614\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "2606\n", + "\n", + " \n", + "bacon\n", + "node score:1.0000\n", + "\n", + "\n", + "\n", + "2607->2606\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "2610\n", + "\n", + " \n", + "onion\n", + "node score:1.0000\n", + "\n", + "\n", + "\n", + "2611->2610\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "2608\n", + "\n", + " \n", + "tomato\n", + "node score:0.6065\n", + "\n", + "\n", + "\n", + "2609->2608\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "2615\n", + "\n", + " \n", + "egg\n", + "node score:0.6065\n", + "\n", + "\n", + "\n", + "2616->2615\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "%3\n", + "\n", + "\n", + "\n", + "2633\n", + "\n", + " \n", + "mix\n", + "node score: 0.6000\n", + "\n", + "\n", + "\n", + "2625\n", + "\n", + " \n", + "chop\n", + "node score: 1.0000\n", + "\n", + "\n", + "\n", + "2633->2625\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "2631\n", + "\n", + " \n", + "cook\n", + "node score: 1.0000\n", + "\n", + "\n", + "\n", + "2633->2631\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "2624\n", + "\n", + " \n", + "tomato\n", + "node score:0.6065\n", + "\n", + "\n", + "\n", + "2625->2624\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "2632\n", + "\n", + " \n", + "mix\n", + "node score: 0.7000\n", + "\n", + "\n", + "\n", + "2631->2632\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "2627\n", + "\n", + " \n", + "chop\n", + "node score: 1.0000\n", + "\n", + "\n", + "\n", + "2632->2627\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "2629\n", + "\n", + " \n", + "cheese\n", + "node score:0.6065\n", + "\n", + "\n", + "\n", + "2632->2629\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "2630\n", + "\n", + " \n", + "salt\n", + "node score:0.0000\n", + "\n", + "\n", + "\n", + "2632->2630\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "2623\n", + "\n", + " \n", + "slice\n", + "node score: 1.0000\n", + "\n", + "\n", + "\n", + "2632->2623\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "2628\n", + "\n", + " \n", + "noodle\n", + "node score:0.6065\n", + "\n", + "\n", + "\n", + "2632->2628\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "2626\n", + "\n", + " \n", + "onion\n", + "node score:1.0000\n", + "\n", + "\n", + "\n", + "2627->2626\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "2622\n", + "\n", + " \n", + "bacon\n", + "node score:1.0000\n", + "\n", + "\n", + "\n", + "2623->2622\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "%3\n", + "\n", + "\n", + "\n", + "2649\n", + "\n", + " \n", + "mix\n", + "node score: 0.1818\n", + "\n", + "\n", + "\n", + "2640\n", + "\n", + " \n", + "cut\n", + "node score: 1.0000\n", + "\n", + "\n", + "\n", + "2649->2640\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "2647\n", + "\n", + " \n", + "cook\n", + "node score: 1.0000\n", + "\n", + "\n", + "\n", + "2649->2647\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "2638\n", + "\n", + " \n", + "cut\n", + "node score: 1.0000\n", + "\n", + "\n", + "\n", + "2649->2638\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "2642\n", + "\n", + " \n", + "cheese\n", + "node score:0.0000\n", + "\n", + "\n", + "\n", + "2649->2642\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "2646\n", + "\n", + " \n", + "rinse\n", + "node score: 1.0000\n", + "\n", + "\n", + "\n", + "2649->2646\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "2639\n", + "\n", + " \n", + "onion\n", + "node score:0.6065\n", + "\n", + "\n", + "\n", + "2640->2639\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "2648\n", + "\n", + " \n", + "mix\n", + "node score: 0.3333\n", + "\n", + "\n", + "\n", + "2647->2648\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "2641\n", + "\n", + " \n", + "noodle\n", + "node score:0.6065\n", + "\n", + "\n", + "\n", + "2648->2641\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "2636\n", + "\n", + " \n", + "dice\n", + "node score: 1.0000\n", + "\n", + "\n", + "\n", + "2648->2636\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "2644\n", + "\n", + " \n", + "olive oil\n", + "node score:0.6065\n", + "\n", + "\n", + "\n", + "2648->2644\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "2643\n", + "\n", + " \n", + "salt\n", + "node score:0.0000\n", + "\n", + "\n", + "\n", + "2648->2643\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "2635\n", + "\n", + " \n", + "bacon\n", + "node score:1.0000\n", + "\n", + "\n", + "\n", + "2636->2635\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "2637\n", + "\n", + " \n", + "tomato\n", + "node score:0.6065\n", + "\n", + "\n", + "\n", + "2638->2637\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "2645\n", + "\n", + " \n", + "spinach\n", + "node score:0.6065\n", + "\n", + "\n", + "\n", + "2646->2645\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" ] }, "metadata": {}, diff --git a/RecipeAnalysis/AdjacencyMatrixRefinement.ipynb b/RecipeAnalysis/AdjacencyMatrixRefinement.ipynb index 858d140..8085519 100644 --- a/RecipeAnalysis/AdjacencyMatrixRefinement.ipynb +++ b/RecipeAnalysis/AdjacencyMatrixRefinement.ipynb @@ -9,7 +9,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -61,7 +61,8 @@ "import settings\n", "import AdjacencyMatrix\n", "from tqdm.autonotebook import tqdm\n", - "from Recipe import Ingredient" + "from Recipe import Ingredient\n", + "from ActionGroups import groups" ] }, { @@ -186,12 +187,12 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "2db5dd15c87740729f16f5c258707db2", + "model_id": "6b079a15f1b94d9e9ebce8ad6eb82058", "version_major": 2, "version_minor": 0 }, "text/plain": [ - "HBox(children=(IntProgress(value=0, max=741659), HTML(value='')))" + "HBox(children=(FloatProgress(value=0.0, max=527544.0), HTML(value='')))" ] }, "metadata": {}, @@ -220,7 +221,7 @@ " \n", " grouped_act = groups[act]\n", " \n", - " m_grouped_act.add_entry(grouped_act, grouped_ing.to_json(),1)\n", + " m_grouped_act.add_entry(grouped_act, grouped_ing.to_json(),c[i_act, j_ing])\n", " " ] }, @@ -232,12 +233,12 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "1d3f802944654954a6abbf8cb70f116e", + "model_id": "b5b6c8ff52d1407495ef7fcd3ff409a8", "version_major": 2, "version_minor": 0 }, "text/plain": [ - "HBox(children=(IntProgress(value=0, max=4472620), HTML(value='')))" + "HBox(children=(FloatProgress(value=0.0, max=2123892.0), HTML(value='')))" ] }, "metadata": {}, @@ -268,7 +269,7 @@ " for a in ing_b._action_set:\n", " grouped_ing_b.apply_action(groups[a])\n", " \n", - " m_grouped_mix.add_entry(grouped_ing_a.to_json(), grouped_ing_b.to_json(),1)" + " m_grouped_mix.add_entry(grouped_ing_a.to_json(), grouped_ing_b.to_json(),c[i_ing, j_ing])" ] }, { @@ -279,12 +280,12 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "92548999e3a6427bb33af20d3e53d9ef", + "model_id": "d4064d730bd34f49946f54b845738585", "version_major": 2, "version_minor": 0 }, "text/plain": [ - "HBox(children=(IntProgress(value=0, max=114804), HTML(value='')))" + "HBox(children=(FloatProgress(value=0.0, max=78714.0), HTML(value='')))" ] }, "metadata": {}, @@ -309,7 +310,7 @@ " \n", " grouped_act = groups[act]\n", " \n", - " m_grouped_base_act.add_entry(grouped_act, base_ing,1)" + " m_grouped_base_act.add_entry(grouped_act, base_ing,c[i_act,j_ing])" ] }, { diff --git a/RecipeAnalysis/MatrixGeneration.ipynb b/RecipeAnalysis/MatrixGeneration.ipynb index 28832dd..ce292f1 100644 --- a/RecipeAnalysis/MatrixGeneration.ipynb +++ b/RecipeAnalysis/MatrixGeneration.ipynb @@ -75,7 +75,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 2, @@ -101,8 +101,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "CPU times: user 8.17 s, sys: 632 ms, total: 8.8 s\n", - "Wall time: 8.84 s\n" + "CPU times: user 8.68 s, sys: 778 ms, total: 9.46 s\n", + "Wall time: 12.5 s\n" ] } ], @@ -181,18 +181,18 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "96abe66e3b4340ea8031c54333c28431", + "model_id": "70396063ce4140b48105751d0ece88c9", "version_major": 2, "version_minor": 0 }, "text/plain": [ - "HBox(children=(IntProgress(value=0, max=100000), HTML(value='')))" + "HBox(children=(FloatProgress(value=0.0, max=100000.0), HTML(value='')))" ] }, "metadata": {}, @@ -202,28 +202,10 @@ "name": "stdout", "output_type": "stream", "text": [ - "warning: recipe 7a6657f1be has no ingredient! skipping it\n", - "warning: recipe 985bf99cb0 has no ingredient! skipping it\n", - "warning: recipe 7b06b89a00 has no ingredient! skipping it\n", - "warning: recipe 5d99d73716 has no ingredient! skipping it\n", - "warning: recipe 5ea97ce121 has no ingredient! skipping it\n", - "warning: recipe 219c14975a has no ingredient! skipping it\n", - "warning: recipe 312dab2c6e has no ingredient! skipping it\n", - "warning: recipe 9847df394d has no ingredient! skipping it\n", - "warning: recipe c456eca29f has no ingredient! skipping it\n", - "warning: recipe 63809adf7d has no ingredient! skipping it\n", - "warning: recipe d8097248d7 has no ingredient! skipping it\n", - "warning: recipe 4f6df45cbe has no ingredient! skipping it\n", - "warning: recipe fa8ffd2e9f has no ingredient! skipping it\n", - "warning: recipe 1274925e80 has no ingredient! skipping it\n", - "warning: recipe 780a3bfa7f has no ingredient! skipping it\n", - "warning: recipe 2f58557f49 has no ingredient! skipping it\n", - "warning: recipe 6e0201877c has no ingredient! skipping it\n", - "warning: recipe 4be587da96 has no ingredient! skipping it\n", - "warning: recipe b4fc8f359d has no ingredient! skipping it\n", - "warning: recipe 83dc617c2a has no ingredient! skipping it\n", - "warning: recipe fe406ab97d has no ingredient! skipping it\n", - "an error occured : 'NoneType' object is not subscriptable\n" + "warning: recipe 0a14762efd has no ingredient! skipping it\n", + "warning: recipe 361b00dc50 has no ingredient! skipping it\n", + "warning: recipe e3b081a317 has no ingredient! skipping it\n", + "an error occured : 'NoneType' object has no attribute 'lower'\n" ] }, { @@ -244,27 +226,39 @@ " return [word2features(sent, i) for i in range(len(sent))]\n", " File \"../Tagging/crf_data_generator.py\", line 87, in \n", " return [word2features(sent, i) for i in range(len(sent))]\n", - " File \"../Tagging/crf_data_generator.py\", line 19, in word2features\n", - " 'word[-3:]=' + word[-3:],\n", - "TypeError: 'NoneType' object is not subscriptable\n" + " File \"../Tagging/crf_data_generator.py\", line 54, in word2features\n", + " '+1:word.lower=' + word1.lower(),\n", + "AttributeError: 'NoneType' object has no attribute 'lower'\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "warning: recipe 83e51c3bc6 has no ingredient! skipping it\n", - "warning: recipe 4178a59023 has no ingredient! skipping it\n", - "warning: recipe 37711ac6d0 has no ingredient! skipping it\n", - "warning: recipe f93a2bfa73 has no ingredient! skipping it\n", - "warning: recipe 5f02077e4c has no ingredient! skipping it\n", - "warning: recipe 3cb76a27d1 has no ingredient! skipping it\n", - "warning: recipe 4c786ec3d6 has no ingredient! skipping it\n", - "warning: recipe 90d70c93c3 has no ingredient! skipping it\n", - "warning: recipe a5f6838027 has no ingredient! skipping it\n", + "warning: recipe 80dd4d10ab has no ingredient! skipping it\n", + "warning: recipe ac1ab87418 has no ingredient! skipping it\n", + "warning: recipe fa5b33cadc has no ingredient! skipping it\n", + "warning: recipe 80eb0385d0 has no ingredient! skipping it\n", + "warning: recipe 2dfb05c466 has no ingredient! skipping it\n", + "warning: recipe 7b372faa16 has no ingredient! skipping it\n", + "warning: recipe f53fe916f2 has no ingredient! skipping it\n", + "warning: recipe b4ff30e1de has no ingredient! skipping it\n", + "warning: recipe cf2300c7ac has no ingredient! skipping it\n", + "warning: recipe d09e7e82f9 has no ingredient! skipping it\n", + "warning: recipe edbdc9c52f has no ingredient! skipping it\n", + "warning: recipe 4db0dadfce has no ingredient! skipping it\n", + "warning: recipe d9933872c6 has no ingredient! skipping it\n", + "warning: recipe 37af7ba84f has no ingredient! skipping it\n", + "warning: recipe cfc02f6bd7 has no ingredient! skipping it\n", + "warning: recipe fac985c58f has no ingredient! skipping it\n", + "warning: recipe 91308ac1b2 has no ingredient! skipping it\n", + "warning: recipe d3ff2bf3f9 has no ingredient! skipping it\n", + "warning: recipe ed9a0d0d51 has no ingredient! skipping it\n", + "warning: recipe 52f19fe220 has no ingredient! skipping it\n", + "warning: recipe 6fa7839c14 has no ingredient! skipping it\n", "\n", - "CPU times: user 2h 14min 8s, sys: 42.9 s, total: 2h 14min 51s\n", - "Wall time: 2h 15min 40s\n" + "CPU times: user 1h 57min 25s, sys: 36.8 s, total: 1h 58min 2s\n", + "Wall time: 1h 58min 41s\n" ] } ], @@ -288,7 +282,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -297,7 +291,7 @@ "99999" ] }, - "execution_count": 9, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -308,7 +302,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -324,7 +318,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -336,7 +330,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -348,7 +342,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -360,7 +354,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ @@ -379,7 +373,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ @@ -398,10 +392,10 @@ "name": "stdout", "output_type": "stream", "text": [ - "(65, 378992) (428558, 428558)\n", - "43498 74724\n", - "(65, 17533) (22178, 22178)\n", - "15465 42881\n" + "(65, 272623) (293753, 293753)\n", + "30381 42885\n", + "(65, 11807) (13603, 13603)\n", + "10725 19775\n" ] } ], @@ -414,15 +408,15 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "(65, 384445) (433582, 433582)\n", - "749954 4526723\n" + "(65, 272623) (293753, 293753)\n", + "30381 42885\n" ] } ], @@ -433,16 +427,16 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "43498" + "30381" ] }, - "execution_count": 16, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" } @@ -453,26 +447,9 @@ }, { "cell_type": "code", - "execution_count": 99, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[1, 1, 0, ..., 0, 0, 0],\n", - " [0, 0, 1, ..., 0, 0, 0],\n", - " [0, 0, 0, ..., 0, 0, 0],\n", - " ...,\n", - " [0, 0, 0, ..., 0, 0, 0],\n", - " [0, 0, 0, ..., 0, 0, 0],\n", - " [0, 0, 0, ..., 0, 0, 0]], dtype=int64)" - ] - }, - "execution_count": 99, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [] }, { diff --git a/RecipeAnalysis/Recipe.ipynb b/RecipeAnalysis/Recipe.ipynb index a2d37d8..ba67b16 100644 --- a/RecipeAnalysis/Recipe.ipynb +++ b/RecipeAnalysis/Recipe.ipynb @@ -344,7 +344,7 @@ " def copy(self):\n", " return RecipeState([ing.copy() for ing in self._ingredients])\n", " \n", - " def apply_action(self, action: str, ing: Ingredient, instruction_number=0, sim_threshold = 0.6):\n", + " def apply_action(self, action: str, ing: Ingredient, instruction_number=0, sim_threshold = 0.6, add_new_if_not_similar=True):\n", " # find most similar ingredient to the given one and apply action on it\n", " sim_val, best_ing = ing.most_similar_ingredient(self._ingredients)\n", " \n", @@ -360,7 +360,7 @@ " self._seen_applied_actions.add((action, best_ing.to_json()))\n", " best_ing.apply_action(action, instruction_number)\n", " self._seen_ingredients.add(best_ing.to_json())\n", - " else:\n", + " elif add_new_if_not_similar:\n", " self._ingredients.append(ing)\n", " if action not in actions.stemmed_mixing_cooking_verbs:\n", " self._seen_actions.add(action)\n", @@ -1284,7 +1284,7 @@ " actions_used.add(action['lemma'])\n", " ingredients_used.add(ing_str)\n", " # apply action on state\n", - " current_state.apply_action(action['lemma'], ing, instruction_number=instruction_number)\n", + " current_state.apply_action(action['lemma'], ing, instruction_number=instruction_number, add_new_if_not_similar=False)\n", " if debug:\n", " print(f\"\\tapply {action['lemma']} on {ing}\")\n", " \n", @@ -1302,7 +1302,7 @@ " actions_used.add(act_token['lemma'])\n", " ingredients_used.add(ing_str)\n", " ing = Ingredient(ing_str)\n", - " current_state.apply_action(act_token['lemma'], ing, instruction_number=instruction_number)\n", + " current_state.apply_action(act_token['lemma'], ing, instruction_number=instruction_number, add_new_if_not_similar=False)\n", " if debug:\n", " print(f\"\\tapply {act_token['lemma']} on {ing}\")\n", " break\n", @@ -1339,7 +1339,7 @@ " print(\"mixing all ingredients in this instruction:\")\n", " \n", " for ing_str in clustered_ingredients:\n", - " current_state.apply_action(\"mix\", Ingredient(ing_str), instruction_number=instruction_number)\n", + " current_state.apply_action(\"mix\", Ingredient(ing_str), instruction_number=instruction_number, add_new_if_not_similar=False)\n", " \n", " for ing in current_state.get_combined_ingredients():\n", " if debug:\n", diff --git a/RecipeAnalysis/Recipe.py b/RecipeAnalysis/Recipe.py index 1c8f745..0854ff6 100644 --- a/RecipeAnalysis/Recipe.py +++ b/RecipeAnalysis/Recipe.py @@ -234,7 +234,7 @@ class RecipeState(object): def copy(self): return RecipeState([ing.copy() for ing in self._ingredients]) - def apply_action(self, action: str, ing: Ingredient, instruction_number=0, sim_threshold = 0.6): + def apply_action(self, action: str, ing: Ingredient, instruction_number=0, sim_threshold = 0.6, add_new_if_not_similar=True): # find most similar ingredient to the given one and apply action on it sim_val, best_ing = ing.most_similar_ingredient(self._ingredients) @@ -250,7 +250,7 @@ class RecipeState(object): self._seen_applied_actions.add((action, best_ing.to_json())) best_ing.apply_action(action, instruction_number) self._seen_ingredients.add(best_ing.to_json()) - else: + elif add_new_if_not_similar: self._ingredients.append(ing) if action not in actions.stemmed_mixing_cooking_verbs: self._seen_actions.add(action) @@ -1150,7 +1150,7 @@ class Recipe(object): actions_used.add(action['lemma']) ingredients_used.add(ing_str) # apply action on state - current_state.apply_action(action['lemma'], ing, instruction_number=instruction_number) + current_state.apply_action(action['lemma'], ing, instruction_number=instruction_number, add_new_if_not_similar=False) if debug: print(f"\tapply {action['lemma']} on {ing}") @@ -1168,7 +1168,7 @@ class Recipe(object): actions_used.add(act_token['lemma']) ingredients_used.add(ing_str) ing = Ingredient(ing_str) - current_state.apply_action(act_token['lemma'], ing, instruction_number=instruction_number) + current_state.apply_action(act_token['lemma'], ing, instruction_number=instruction_number, add_new_if_not_similar=False) if debug: print(f"\tapply {act_token['lemma']} on {ing}") break @@ -1205,7 +1205,7 @@ class Recipe(object): print("mixing all ingredients in this instruction:") for ing_str in clustered_ingredients: - current_state.apply_action("mix", Ingredient(ing_str), instruction_number=instruction_number) + current_state.apply_action("mix", Ingredient(ing_str), instruction_number=instruction_number, add_new_if_not_similar=False) for ing in current_state.get_combined_ingredients(): if debug: diff --git a/Vocabulary/Vocabulary Creation.ipynb b/Vocabulary/Vocabulary Creation.ipynb index ed88b72..e66390d 100644 --- a/Vocabulary/Vocabulary Creation.ipynb +++ b/Vocabulary/Vocabulary Creation.ipynb @@ -14575,7 +14575,7 @@ } } }, - "image/png": "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", + "image/png": "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", "text/html": [ "
\n", " \n", @@ -15660,7 +15660,7 @@ } } }, - "image/png": "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", + "image/png": "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", "text/html": [ "
\n", " \n", @@ -16276,7 +16276,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.3" + "version": "3.7.5rc1" } }, "nbformat": 4,