diff --git a/Project/simple_approach/simple_twitter_learning.ipynb b/Project/simple_approach/simple_twitter_learning.ipynb index bb6f0a5..2c5bd06 100644 --- a/Project/simple_approach/simple_twitter_learning.ipynb +++ b/Project/simple_approach/simple_twitter_learning.ipynb @@ -2412,6 +2412,86 @@ "display(testlist)\n" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* exactly correct labeled sentences:" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.21221374045801528\n" + ] + } + ], + "source": [ + "print (sum([1 if sample[1]['teacher'] == sample[1]['predict'] else 0 for sample in testlist.iterrows()]) / testlist.shape[0])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* mean squared error:" + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0.02218067, 0.02490165, 0.00302656])" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "teacher_sentiments = np.array([sample[1]['teacher_sentiment'] for sample in testlist.iterrows()])\n", + "predicted_sentiments = np.array([sample[1]['predicted_sentiment'] for sample in testlist.iterrows()])\n", + "\n", + "mean_squared_error = ((teacher_sentiments - predicted_sentiments)**2).mean(axis=0)\n", + "display(mean_squared_error)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* by an overall variance of:" + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Variance teacher: [0.02183094 0.02513847 0.00285735]\n", + "Variance prediction: [0.00863898 0.00919997 0.00082446]\n" + ] + } + ], + "source": [ + "print(\"Variance teacher: \", np.var(teacher_sentiments, axis=0))\n", + "print(\"Variance prediction: \", np.var(predicted_sentiments, axis=0))" + ] + }, { "cell_type": "code", "execution_count": 35, @@ -2541,13 +2621,13 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 40, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "9c34c24b4df140f9ab22ded811cf8f89", + "model_id": "8a4248b8f8d147a58cdc02f4be7fbe03", "version_major": 2, "version_minor": 0 }, @@ -2561,12 +2641,12 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "7fb503b1cbb9472da347289cc130f838", + "model_id": "525901f072994db2a57e654faa5b9b39", "version_major": 2, "version_minor": 0 }, "text/plain": [ - "VBox(children=(Button(description='get smiley', icon='check', style=ButtonStyle(), tooltip='Click me'), Output…" + "VBox(children=(Button(description='get emoji', icon='check', style=ButtonStyle(), tooltip='Click me'), Output(…" ] }, "metadata": {}, @@ -2578,7 +2658,7 @@ "\n", "t = widgets.Text()\n", "b = widgets.Button(\n", - " description='get smiley',\n", + " description='get emoji',\n", " disabled=False,\n", " button_style='', # 'success', 'info', 'warning', 'danger' or ''\n", " tooltip='Click me',\n", @@ -2593,7 +2673,7 @@ " with out:\n", " pred = clf.predict(vectorizer.transform([t.value]))\n", " \n", - " display(Markdown(\"# \" + str(sent2emoji(pred, lookup_emojis))))\n", + " display(Markdown(\"# \" + str(sent2emoji(pred, lookup_emojis)[0])))\n", "\n", "b.on_click(handle_submit)\n", " \n", @@ -2627,10 +2707,8 @@ ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "metadata": {}, - "outputs": [], "source": [] } ],