def predict() :
data = flask.request.json
print("in service")
print(data)
titanic_test = pd.DataFrame(data)
#print(titanic_test.info())
model_objects = joblib.load(os.path.join(dir,'titanic_model_1.pkl') )
titanic_test1 = utils.drop_features(titanic_test, ['PassengerId', 'Name', 'Ticket', 'Cabin'])
utils.cast_to_cat(titanic_test1, ['Sex', 'Pclass', 'Embarked'])
cat_features = utils.get_categorical_features(titanic_test1)
#print(cat_features)
cont_features = utils.get_continuous_features(titanic_test1)
#print(cont_features)
titanic_test1[cat_features] = model_objects.get('cat_imputers').transform(titanic_test1[cat_features])
titanic_test1[cont_features] = model_objects.get('cont_imputers').transform(titanic_test1[cont_features])
utils.cast_to_cat(titanic_test1, ['Sex', 'Pclass', 'Embarked'])
titanic_test1['Sex'] = titanic_test1['Sex'].cat.add_categories(['male', 'female'])
titanic_test1['Pclass'] = titanic_test1['Pclass'].cat.add_categories([1,2,3])
titanic_test1['Embarked'] = titanic_test1['Embarked'].cat.add_categories(['S','Q','C'])
#print(titanic_test1.info())
titanic_test2 = utils.ohe(titanic_test1, cat_features)
print(titanic_test2.shape)
X_test = model_objects.get('scaler').transform(titanic_test2)
result = model_objects.get('estimator').predict(X_test)
print(result)
return flask.jsonify(prediction=str(1))
house_test.shape
house_test.info()
house = pd.concat((house_train, house_test), axis=0)
house.shape
house.info()
features_to_cast = ['MSSubClass']
cast_cont_to_cat(house, features_to_cast)
print(get_continuous_features(house))
print(get_categorical_features(house))
features_to_drop = ['Id', 'SalePrice']
features_to_drop.extend(get_features_to_drop_on_missingdata(house, 0.25))
house1 = drop_features(house, features_to_drop)
house1.info()
imputable_cat_features = get_categorical_features(house1)
cat_imputer = get_categorical_imputers(house1, imputable_cat_features)
house1[imputable_cat_features] = cat_imputer.transform(
house1[imputable_cat_features])
imputable_cont_features = get_continuous_features(house1)
cont_imputer = get_continuous_imputers(house1, imputable_cont_features)
house1[imputable_cont_features] = cont_imputer.transform(
house1[imputable_cont_features])
house1.info()
house2 = ohe(house1, imputable_cat_features)
import pandas as pd
import numpy as np
import os
import common_utils as utils
from sklearn import preprocessing, neighbors, svm, linear_model, ensemble, pipeline, model_selection
import classification_utils as cutils
import kernel_utils as kutils
dir = 'E:/'
titanic_train = pd.read_csv(os.path.join(dir, 'train.csv'))
print(titanic_train.shape)
print(titanic_train.info())
titanic_train1 = utils.drop_features(
titanic_train, ['PassengerId', 'Name', 'Survived', 'Ticket', 'Cabin'])
#type casting
utils.cast_to_cat(titanic_train1, ['Sex', 'Pclass', 'Embarked'])
cat_features = utils.get_categorical_features(titanic_train1)
print(cat_features)
cont_features = utils.get_continuous_features(titanic_train1)
print(cont_features)
#handle missing data(imputation)
cat_imputers = utils.get_categorical_imputers(titanic_train1, cat_features)
titanic_train1[cat_features] = cat_imputers.transform(
titanic_train1[cat_features])
cont_imputers = utils.get_continuous_imputers(titanic_train1, cont_features)
titanic_train1[cont_features] = cont_imputers.transform(
return 'Medium'
else:
return 'Large'
titanic['FamilyGroup'] = titanic['FamilySize'].map(convert_familysize)
sns.factorplot(x="FamilyGroup",
hue="Survived",
data=titanic,
kind="count",
size=6)
sns.countplot(x='Cabin', data=titanic)
titanic['Cabin'] = titanic['Cabin'].fillna('U')
titanic = utils.drop_features(titanic,
['PassengerId', 'Name', 'Survived', 'Ticket'])
#type casting
utils.cast_to_cat(
titanic, ['Sex', 'Pclass', 'Embarked', 'Title', 'FamilyGroup', 'Cabin'])
cat_features = utils.get_categorical_features(titanic)
print(cat_features)
cont_features = utils.get_continuous_features(titanic)
print(cont_features)
#handle missing data(imputation)
cat_imputers = utils.get_categorical_imputers(titanic, cat_features)
titanic[cat_features] = cat_imputers.transform(titanic[cat_features])
cont_imputers = utils.get_continuous_imputers(titanic, cont_features)
titanic[cont_features] = cont_imputers.transform(titanic[cont_features])
path = 'G://house-prices'
house_train = pd.read_csv(os.path.join(path, "train.csv"))
house_train.shape
house_train.info()
#type cast features
features_to_cast = ['MSSubClass']
utils.cast_to_cat(house_train, features_to_cast)
#manual feature selection
features_to_drop = ['Id', 'SalePrice']
missing_features_above_th = utils.get_features_to_drop_on_missingdata(
house_train, 0.25)
features_to_drop.extend(missing_features_above_th)
house_train1 = utils.drop_features(house_train, features_to_drop)
house_train1.info()
#build pipeline for categorical features
categorical_pipeline = pipeline.Pipeline([
('imputer', impute.SimpleImputer(strategy="most_frequent")),
('ohe', preprocessing.OneHotEncoder(sparse=False, handle_unknown='ignore'))
])
#build pipeline for numerical features
numerical_pipeline = pipeline.Pipeline([('imputer', impute.SimpleImputer()),
('scaler',
preprocessing.StandardScaler())])
#build preprocessing pipeline for all features
cat_features = utils.get_non_continuous_features(house_train1)
sns.FacetGrid(house_train, hue="YearBuilt",
size=8).map(sns.kdeplot, "SalePrice").add_legend()
sns.countplot(x='YrSold', data=house_train)
sns.jointplot(x="SalePrice", y="YrSold", data=house_train)
sns.FacetGrid(house_train, hue="YrSold", size=8).map(sns.kdeplot,
"SalePrice").add_legend()
features_to_cast = ['MSSubClass']
utils.cast_to_cat(house, features_to_cast)
features_to_drop = ['Id']
missing_features_above_th = utils.get_features_to_drop_on_missingdata(
house, 0.25)
features_to_drop.extend(missing_features_above_th)
house1 = utils.drop_features(house, features_to_drop)
house1.info()
imputable_cat_features = utils.get_non_continuous_features(house1)
cat_imputer = utils.get_categorical_imputers(house1, imputable_cat_features)
house1[imputable_cat_features] = cat_imputer.transform(
house1[imputable_cat_features])
imputable_cont_features = utils.get_continuous_features(house1)
cont_imputer = utils.get_continuous_imputers(house1, imputable_cont_features)
house1[imputable_cont_features] = cont_imputer.transform(
house1[imputable_cont_features])
house1.info()
house2 = utils.ohe(house1, imputable_cat_features)
