From 89d49ea8c95c55ee42049b73fe0885e991a0ae77 Mon Sep 17 00:00:00 2001 From: pawithlearning Date: Wed, 15 Jun 2022 14:37:22 +0700 Subject: [PATCH 1/3] add Tigerdiego class --- aibuildersart/asciiart.py | 50 +++++++++++++++++++++++++++++++++++++++ 1 file changed, 50 insertions(+) diff --git a/aibuildersart/asciiart.py b/aibuildersart/asciiart.py index 8773e45..bac0d64 100644 --- a/aibuildersart/asciiart.py +++ b/aibuildersart/asciiart.py @@ -333,3 +333,53 @@ def __str__(self): return self.name +class Tigerdiego: + ''' + test = UncleEngineer() + test.art() + ''' + + def __init__(self): + self.name = 'pawithclass' + + def art(self): + asciiart = ''' + ,-. ______ ,. + ("` )-,-' -. `-( / + `._`- _,._` ,';. + _/ ---,',o-.` ,-,:o. + /,:"' ,-.`-, ``"\ + / ,.::"(' ` ,::. \ + (;:' __,' \ \_ `"""``../ + :',' ; \`._ `') + ' "": ;; \| |`----; + ,' ,- '` ; `| |`==='\. + ' ; | | | \ + / ; ; ; ; ) ` + / , '/ /, // ; + ; , /,';;;;;// ; + : :' ` ;/ ' + | :: ; ; + | .::: ; + | `: , : + ; | , ; + -hrr- \ | :: | + ,\ |. :: | + / \ :. .:' \ + ,' ; ;:. .:; | + ,-""-.,^-`; ::::.::,'| ; + /:. ` `; |:::::/ | : + (:::. ` ; |:::'/ | | + \:' ` `. \ \' / | | + \ ` \:.:\ \ / : : + ,'`-'`. \::; `/ ; ( +,;_ `._ \:/ `--.,' `. + `::. \ ,::'Y-. ,-. ,-. ( ,-. ,-.`,. + `"`--.__) ( Y Y \;\ ( Y Y \ + \ ; | :| : | : | \| :| :| :| + `-'`--;^-;-^-;-' `-; `-;`-;' + ''' + print(asciiart) + + def __str__(self): + return self.name \ No newline at end of file From 3171eb4e08368a284f7abdaba4e8786d80452f3f Mon Sep 17 00:00:00 2001 From: pawithlearning Date: Wed, 15 Jun 2022 22:06:27 +0700 Subject: [PATCH 2/3] new file ipynb --- .../Machine learning-checkpoint.ipynb | 71 +++++++++++++++++++ 1 file changed, 71 insertions(+) create mode 100644 aibuildersart/Machine learning-checkpoint.ipynb diff --git a/aibuildersart/Machine learning-checkpoint.ipynb b/aibuildersart/Machine learning-checkpoint.ipynb new file mode 100644 index 0000000..e9f99ee --- /dev/null +++ b/aibuildersart/Machine learning-checkpoint.ipynb @@ -0,0 +1,71 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "from sklearn.compose import make_column_transformer\n", + "from sklearn.preprocessing import OneHotEncoder, StandardScaler\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.neighbors import KNeighborsClassifier\n", + "from sklearn.model_selection import GridSearchCV\n", + "from sklearn.model_selection import cross_val_score\n", + "from itertools import combinations\n", + "import numpy as np\n", + "df = pd.read_csv(\"C:/Users/pawithlocal/pawith\\program_coding/KUxDEPA2022/Datasets/Telco-Customer-Churn.csv\")\n", + "column_object = [i for i in df.columns if i not in ['SeniorCitizen', 'tenure', 'MonthlyCharges', 'TotalCharges',\n", + " 'Churn', 'total_usage_month','customerID']]\n", + "X = df[column_object]\n", + "y = df[[\"Churn\"]]\n", + "y.info()\n", + "X_train, X_test, y_train, y_test = train_test_split(X, y, \n", + " test_size=0.2, \n", + " train_size=0.8)\n", + "import warnings\n", + "\n", + "with warnings.catch_warnings():\n", + " # this will suppress all warnings in this block\n", + " warnings.simplefilter(\"ignore\")\n", + " # compute combination of n columns that select to be features\n", + " \n", + " n_col_select = 2\n", + " comb = list(combinations([i for i in range(0,len(column_object),1)], n_col_select))\n", + " comb = np.array(comb)\n", + " col_object_np = np.array(column_object)\n", + " train_score = []\n", + " test_score = []\n", + " col_index = []\n", + " for col in comb:\n", + " col_selected = list(col_object_np[col]) # columns that have selected\n", + "\n", + " transformer = make_column_transformer(\n", + " (StandardScaler(), []),\n", + " (OneHotEncoder(), col_object_np[col]),\n", + " remainder = \"passthrough\")\n", + "\n", + " X_transformed_train = transformer.fit_transform(X_train[col_selected])\n", + " X_transformed_test = transformer.fit_transform(X_test[col_selected])\n", + " n = 14\n", + " knn = KNeighborsClassifier(n_neighbors=n)\n", + " knn.fit(X_transformed_train, y_train)\n", + " train_score.append(knn.score(X_transformed_train, y_train))\n", + " test_score.append(knn.score(X_transformed_test, y_test))\n", + "\n", + " col_index.append(col_selected)\n", + "a = pd.DataFrame({\"column\":col_index, \"train_score\":train_score, \"test_score\":test_score})\n", + "a.sort_values([\"test_score\"], ascending=False)\n", + "a" + ] + } + ], + "metadata": { + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From 988be393a86eef74bcab0753fedabfa9545a87bf Mon Sep 17 00:00:00 2001 From: pawithgunlearning <104490912+pawithgunlearning@users.noreply.github.com> Date: Wed, 15 Jun 2022 22:12:00 +0700 Subject: [PATCH 3/3] Delete Machine learning-checkpoint.ipynb --- .../Machine learning-checkpoint.ipynb | 71 ------------------- 1 file changed, 71 deletions(-) delete mode 100644 aibuildersart/Machine learning-checkpoint.ipynb diff --git a/aibuildersart/Machine learning-checkpoint.ipynb b/aibuildersart/Machine learning-checkpoint.ipynb deleted file mode 100644 index e9f99ee..0000000 --- a/aibuildersart/Machine learning-checkpoint.ipynb +++ /dev/null @@ -1,71 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "from sklearn.compose import make_column_transformer\n", - "from sklearn.preprocessing import OneHotEncoder, StandardScaler\n", - "from sklearn.model_selection import train_test_split\n", - "from sklearn.neighbors import KNeighborsClassifier\n", - "from sklearn.model_selection import GridSearchCV\n", - "from sklearn.model_selection import cross_val_score\n", - "from itertools import combinations\n", - "import numpy as np\n", - "df = pd.read_csv(\"C:/Users/pawithlocal/pawith\\program_coding/KUxDEPA2022/Datasets/Telco-Customer-Churn.csv\")\n", - "column_object = [i for i in df.columns if i not in ['SeniorCitizen', 'tenure', 'MonthlyCharges', 'TotalCharges',\n", - " 'Churn', 'total_usage_month','customerID']]\n", - "X = df[column_object]\n", - "y = df[[\"Churn\"]]\n", - "y.info()\n", - "X_train, X_test, y_train, y_test = train_test_split(X, y, \n", - " test_size=0.2, \n", - " train_size=0.8)\n", - "import warnings\n", - "\n", - "with warnings.catch_warnings():\n", - " # this will suppress all warnings in this block\n", - " warnings.simplefilter(\"ignore\")\n", - " # compute combination of n columns that select to be features\n", - " \n", - " n_col_select = 2\n", - " comb = list(combinations([i for i in range(0,len(column_object),1)], n_col_select))\n", - " comb = np.array(comb)\n", - " col_object_np = np.array(column_object)\n", - " train_score = []\n", - " test_score = []\n", - " col_index = []\n", - " for col in comb:\n", - " col_selected = list(col_object_np[col]) # columns that have selected\n", - "\n", - " transformer = make_column_transformer(\n", - " (StandardScaler(), []),\n", - " (OneHotEncoder(), col_object_np[col]),\n", - " remainder = \"passthrough\")\n", - "\n", - " X_transformed_train = transformer.fit_transform(X_train[col_selected])\n", - " X_transformed_test = transformer.fit_transform(X_test[col_selected])\n", - " n = 14\n", - " knn = KNeighborsClassifier(n_neighbors=n)\n", - " knn.fit(X_transformed_train, y_train)\n", - " train_score.append(knn.score(X_transformed_train, y_train))\n", - " test_score.append(knn.score(X_transformed_test, y_test))\n", - "\n", - " col_index.append(col_selected)\n", - "a = pd.DataFrame({\"column\":col_index, \"train_score\":train_score, \"test_score\":test_score})\n", - "a.sort_values([\"test_score\"], ascending=False)\n", - "a" - ] - } - ], - "metadata": { - "language_info": { - "name": "python" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -}