diff --git a/lec3.ipynb b/lec3.ipynb index 25ba91c..11db3f8 100644 --- a/lec3.ipynb +++ b/lec3.ipynb @@ -2220,6 +2220,319 @@ "pd.concat([titanic[\"Age\"], pd.qcut(titanic[\"Age\"], q=3, labels=labels)], axis=1).head(20)" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Пример конструирования признаков на основе существующих\n", + "\n", + "Title - обращение к пассажиру (Mr, Mrs, Miss)\n", + "\n", + "Is_married - замужняя ли женщина\n", + "\n", + "Cabin_type - палуба (тип каюты)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
SurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarkedTitleIs_marriedCabin_type
21.01.0Cumings, Mrs. John Bradley (Florence Briggs Th...female38.01.00.0PC 1759971.2833C85CMrs1C
41.01.0Futrelle, Mrs. Jacques Heath (Lily May Peel)female35.01.00.011380353.1000C123SMrs1C
70.01.0McCarthy, Mr. Timothy Jmale54.00.00.01746351.8625E46SMr0E
111.03.0Sandstrom, Miss. Marguerite Rutfemale4.01.01.0PP 954916.7000G6SMiss0G
121.01.0Bonnell, Miss. Elizabethfemale58.00.00.011378326.5500C103SMiss0C
.............................................
8721.01.0Beckwith, Mrs. Richard Leonard (Sallie Monypeny)female47.01.01.01175152.5542D35SMrs1D
8730.01.0Carlsson, Mr. Frans Olofmale33.00.00.06955.0000B51 B53 B55SMr0B
8801.01.0Potter, Mrs. Thomas Jr (Lily Alexenia Wilson)female56.00.01.01176783.1583C50CMrs1C
8881.01.0Graham, Miss. Margaret Edithfemale19.00.00.011205330.0000B42SMiss0B
8901.01.0Behr, Mr. Karl Howellmale26.00.00.011136930.0000C148CMr0C
\n", + "

183 rows × 14 columns

\n", + "
" + ], + "text/plain": [ + " Survived Pclass Name \\\n", + "2 1.0 1.0 Cumings, Mrs. John Bradley (Florence Briggs Th... \n", + "4 1.0 1.0 Futrelle, Mrs. Jacques Heath (Lily May Peel) \n", + "7 0.0 1.0 McCarthy, Mr. Timothy J \n", + "11 1.0 3.0 Sandstrom, Miss. Marguerite Rut \n", + "12 1.0 1.0 Bonnell, Miss. Elizabeth \n", + ".. ... ... ... \n", + "872 1.0 1.0 Beckwith, Mrs. Richard Leonard (Sallie Monypeny) \n", + "873 0.0 1.0 Carlsson, Mr. Frans Olof \n", + "880 1.0 1.0 Potter, Mrs. Thomas Jr (Lily Alexenia Wilson) \n", + "888 1.0 1.0 Graham, Miss. Margaret Edith \n", + "890 1.0 1.0 Behr, Mr. Karl Howell \n", + "\n", + " Sex Age SibSp Parch Ticket Fare Cabin Embarked \\\n", + "2 female 38.0 1.0 0.0 PC 17599 71.2833 C85 C \n", + "4 female 35.0 1.0 0.0 113803 53.1000 C123 S \n", + "7 male 54.0 0.0 0.0 17463 51.8625 E46 S \n", + "11 female 4.0 1.0 1.0 PP 9549 16.7000 G6 S \n", + "12 female 58.0 0.0 0.0 113783 26.5500 C103 S \n", + ".. ... ... ... ... ... ... ... ... \n", + "872 female 47.0 1.0 1.0 11751 52.5542 D35 S \n", + "873 male 33.0 0.0 0.0 695 5.0000 B51 B53 B55 S \n", + "880 female 56.0 0.0 1.0 11767 83.1583 C50 C \n", + "888 female 19.0 0.0 0.0 112053 30.0000 B42 S \n", + "890 male 26.0 0.0 0.0 111369 30.0000 C148 C \n", + "\n", + " Title Is_married Cabin_type \n", + "2 Mrs 1 C \n", + "4 Mrs 1 C \n", + "7 Mr 0 E \n", + "11 Miss 0 G \n", + "12 Miss 0 C \n", + ".. ... ... ... \n", + "872 Mrs 1 D \n", + "873 Mr 0 B \n", + "880 Mrs 1 C \n", + "888 Miss 0 B \n", + "890 Mr 0 C \n", + "\n", + "[183 rows x 14 columns]" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "titanic_cl = titanic.drop(\n", + " [\"Embarked_Q\", \"Embarked_S\", \"Embarked_nan\", \"Sex_male\"], axis=1, errors=\"ignore\"\n", + ")\n", + "titanic_cl = titanic_cl.dropna()\n", + "\n", + "titanic_cl[\"Title\"] = [\n", + " i.split(\",\")[1].split(\".\")[0].strip() for i in titanic_cl[\"Name\"]\n", + "]\n", + "\n", + "titanic_cl[\"Is_married\"] = [1 if i == \"Mrs\" else 0 for i in titanic_cl[\"Title\"]]\n", + "\n", + "titanic_cl[\"Cabin_type\"] = [i[0] for i in titanic_cl[\"Cabin\"]]\n", + "\n", + "titanic_cl" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -2244,7 +2557,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 17, "metadata": {}, "outputs": [], "source": [ @@ -2276,7 +2589,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 18, "metadata": {}, "outputs": [ { @@ -2331,7 +2644,7 @@ " No relationships" ] }, - "execution_count": 17, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" } @@ -2408,7 +2721,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 19, "metadata": {}, "outputs": [ { @@ -2428,7 +2741,7 @@ " order_items.seller_id -> sellers.seller_id" ] }, - "execution_count": 18, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" } @@ -2455,7 +2768,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 20, "metadata": {}, "outputs": [ { @@ -2466,11 +2779,11 @@ " agg_primitives: ['any', 'mode']\n", "This may be caused by a using a value of max_depth that is too small, not setting interesting values, or it may indicate no compatible columns for the primitive were found in the data. If the DFS call contained multiple instances of a primitive in the list above, none of them were used.\n", " warnings.warn(warning_msg, UnusedPrimitiveWarning)\n", - "c:\\Users\\user\\Projects\\python\\mai\\.venv\\Lib\\site-packages\\featuretools\\computational_backends\\feature_set_calculator.py:785: FutureWarning: The provided callable is currently using SeriesGroupBy.mean. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string \"mean\" instead.\n", + "c:\\Users\\user\\Projects\\python\\mai\\.venv\\Lib\\site-packages\\featuretools\\computational_backends\\feature_set_calculator.py:785: FutureWarning: The provided callable is currently using SeriesGroupBy.mean. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string \"mean\" instead.\n", " ).agg(to_agg)\n", - "c:\\Users\\user\\Projects\\python\\mai\\.venv\\Lib\\site-packages\\featuretools\\computational_backends\\feature_set_calculator.py:785: FutureWarning: The provided callable is currently using SeriesGroupBy.mean. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string \"mean\" instead.\n", + "c:\\Users\\user\\Projects\\python\\mai\\.venv\\Lib\\site-packages\\featuretools\\computational_backends\\feature_set_calculator.py:785: FutureWarning: The provided callable is currently using SeriesGroupBy.mean. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string \"mean\" instead.\n", " ).agg(to_agg)\n", - "c:\\Users\\user\\Projects\\python\\mai\\.venv\\Lib\\site-packages\\featuretools\\computational_backends\\feature_set_calculator.py:785: FutureWarning: The provided callable is currently using SeriesGroupBy.mean. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string \"mean\" instead.\n", + "c:\\Users\\user\\Projects\\python\\mai\\.venv\\Lib\\site-packages\\featuretools\\computational_backends\\feature_set_calculator.py:785: FutureWarning: The provided callable is currently using SeriesGroupBy.mean. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string \"mean\" instead.\n", " ).agg(to_agg)\n" ] }, @@ -2984,7 +3297,7 @@ "[115 rows x 43 columns]" ] }, - "execution_count": 19, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" } @@ -3012,7 +3325,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 21, "metadata": {}, "outputs": [ { @@ -3063,7 +3376,7 @@ " ]" ] }, - "execution_count": 20, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" } @@ -3088,7 +3401,7 @@ }, { "cell_type": "code", - "execution_count": 148, + "execution_count": 22, "metadata": {}, "outputs": [ { @@ -3097,9 +3410,19 @@ "" ] }, - "execution_count": 148, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ @@ -3115,7 +3438,7 @@ }, { "cell_type": "code", - "execution_count": 149, + "execution_count": 23, "metadata": {}, "outputs": [ { @@ -3209,7 +3532,7 @@ "852 Svensson, Mr. Johan 74.0 65.0" ] }, - "execution_count": 149, + "execution_count": 23, "metadata": {}, "output_type": "execute_result" } @@ -3231,7 +3554,7 @@ }, { "cell_type": "code", - "execution_count": 150, + "execution_count": 24, "metadata": {}, "outputs": [ { @@ -3332,7 +3655,7 @@ "852 Svensson, Mr. Johan 74.0 54.0" ] }, - "execution_count": 150, + "execution_count": 24, "metadata": {}, "output_type": "execute_result" } @@ -3358,7 +3681,7 @@ }, { "cell_type": "code", - "execution_count": 153, + "execution_count": 25, "metadata": {}, "outputs": [ { @@ -3621,7 +3944,7 @@ "20 NaN 0.546456 0.092912 " ] }, - "execution_count": 153, + "execution_count": 25, "metadata": {}, "output_type": "execute_result" } @@ -3663,7 +3986,7 @@ }, { "cell_type": "code", - "execution_count": 152, + "execution_count": 26, "metadata": {}, "outputs": [ { @@ -3905,7 +4228,7 @@ "20 NaN 0.031205 " ] }, - "execution_count": 152, + "execution_count": 26, "metadata": {}, "output_type": "execute_result" }