E2E ML Project¶

In [ ]:

# @title Setup
# Common imports
import numpy as np
import pandas as pd
import os


# To plot pretty figures
%matplotlib inline
import matplotlib as mpl
import matplotlib.pyplot as plt
mpl.rc('axes', labelsize=14)
mpl.rc('xtick', labelsize=12)
mpl.rc('ytick', labelsize=12)

# @title Setup # Common imports import numpy as np import pandas as pd import os # To plot pretty figures %matplotlib inline import matplotlib as mpl import matplotlib.pyplot as plt mpl.rc('axes', labelsize=14) mpl.rc('xtick', labelsize=12) mpl.rc('ytick', labelsize=12)

Data¶

• Retrieve
• Explore

Automate data retrieval process¶

0. Create appropriate directories using os.path and os.mkdirs
1. Download the raw data file using urllib.request.urlretreive
2. Extract it using tarfile.open and .extractall function

In [ ]:

import os
import tarfile
import urllib.request

DOWNLOAD_ROOT = "https://raw.githubusercontent.com/ageron/handson-ml2/master/"
HOUSING_PATH = os.path.join("../","datasets", "housing")
HOUSING_URL = DOWNLOAD_ROOT + "datasets/housing/housing.tgz"

def fetch_housing_data(housing_url=HOUSING_URL, housing_path=HOUSING_PATH):
    if not os.path.isdir(housing_path):
        os.makedirs(housing_path)
    tgz_path = os.path.join(housing_path, "housing.tgz")
    urllib.request.urlretrieve(housing_url, tgz_path)
    housing_tgz = tarfile.open(tgz_path)
    housing_tgz.extractall(path=housing_path)
    housing_tgz.close()

fetch_housing_data()

import os import tarfile import urllib.request DOWNLOAD_ROOT = "https://raw.githubusercontent.com/ageron/handson-ml2/master/" HOUSING_PATH = os.path.join("../","datasets", "housing") HOUSING_URL = DOWNLOAD_ROOT + "datasets/housing/housing.tgz" def fetch_housing_data(housing_url=HOUSING_URL, housing_path=HOUSING_PATH): if not os.path.isdir(housing_path): os.makedirs(housing_path) tgz_path = os.path.join(housing_path, "housing.tgz") urllib.request.urlretrieve(housing_url, tgz_path) housing_tgz = tarfile.open(tgz_path) housing_tgz.extractall(path=housing_path) housing_tgz.close() fetch_housing_data()

In [ ]:

import pandas as pd

def load_housing_data(housing_path=HOUSING_PATH):
    csv_path = os.path.join(housing_path, "housing.csv")
    return pd.read_csv(csv_path)

housing = load_housing_data()

import pandas as pd def load_housing_data(housing_path=HOUSING_PATH): csv_path = os.path.join(housing_path, "housing.csv") return pd.read_csv(csv_path) housing = load_housing_data()

Explore¶

Don't modify the original raw data...

• Descriptive statistics

  • .info()
  • .describe()
  • sorted counts
  • Sorted Correlation matrix
  • Create new categories from quantitative data using pd.cut()

• Split the data

  • Into
    • Train
    • Test
    • Exploration (optional)
  • using StratifiedShuffleSplit (maintain strata)

Descriptive Statistics¶

• Observe the most seemingly important variables. Use judgement

In [ ]:

housing.info()

housing.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 20640 entries, 0 to 20639
Data columns (total 10 columns):
 #   Column              Non-Null Count  Dtype  
---  ------              --------------  -----  
 0   longitude           20640 non-null  float64
 1   latitude            20640 non-null  float64
 2   housing_median_age  20640 non-null  float64
 3   total_rooms         20640 non-null  float64
 4   total_bedrooms      20433 non-null  float64
 5   population          20640 non-null  float64
 6   households          20640 non-null  float64
 7   median_income       20640 non-null  float64
 8   median_house_value  20640 non-null  float64
 9   ocean_proximity     20640 non-null  object 
dtypes: float64(9), object(1)
memory usage: 1.6+ MB

In [ ]:

housing['median_house_value'].value_counts().sort_values()
housing['median_income'].describe()

housing['median_house_value'].value_counts().sort_values() housing['median_income'].describe()

Out[ ]:

	median_income
count	20640.000000
mean	3.870671
std	1.899822
min	0.499900
25%	2.563400
50%	3.534800
75%	4.743250
max	15.000100

dtype: float64

In [ ]:

corr_matrix = housing.drop(columns=['ocean_proximity']).corr()
corr_matrix['median_house_value'].sort_values(ascending=False)

corr_matrix = housing.drop(columns=['ocean_proximity']).corr() corr_matrix['median_house_value'].sort_values(ascending=False)

Out[ ]:

	median_house_value
median_house_value	1.000000
median_income	0.688075
total_rooms	0.134153
housing_median_age	0.105623
households	0.065843
total_bedrooms	0.049686
population	-0.024650
longitude	-0.045967
latitude	-0.144160

dtype: float64

In [ ]:

housing['income_cat'] = pd.cut(housing['median_income'],
       bins=[0.,1.5,3.0,4.5, 6, np.inf],
       labels = [1, 2, 3, 4, 5]
       )

housing['income_cat'] = pd.cut(housing['median_income'], bins=[0.,1.5,3.0,4.5, 6, np.inf], labels = [1, 2, 3, 4, 5] )

Split Data¶

• Split with the same distribution in test and train so that similar income categories end up in each

In [ ]:

from sklearn.model_selection import StratifiedShuffleSplit

stratified = StratifiedShuffleSplit(n_splits=1, test_size=0.2, random_state=42)
for test_index, train_index in stratified.split(housing, housing['income_cat']):
    strat_train_set = housing.loc[test_index]
    strat_test_set = housing.loc[train_index]

strat_train_set.info()

from sklearn.model_selection import StratifiedShuffleSplit stratified = StratifiedShuffleSplit(n_splits=1, test_size=0.2, random_state=42) for test_index, train_index in stratified.split(housing, housing['income_cat']): strat_train_set = housing.loc[test_index] strat_test_set = housing.loc[train_index] strat_train_set.info()

Define Relevant Variables

Visualize & Gain insights¶

• barplot/piechart
• scatter plot (with proper channels of information: color, shape, size etc)
• scatter_matrix

In [ ]:

strat_train_set['income_cat'].hist()
plt.show()

strat_train_set['income_cat'].hist() plt.show()

In [ ]:

print(strat_train_set['median_income'].describe())
strat_train_set['median_income'].hist()
plt.show()

print(strat_train_set['median_income'].describe()) strat_train_set['median_income'].hist() plt.show()

count    16512.000000
mean         3.875884
std          1.904931
min          0.499900
25%          2.566950
50%          3.541550
75%          4.745325
max         15.000100
Name: median_income, dtype: float64

In [ ]:

def make_pie(a):
    print(a.values)
    plt.pie(a,labels=a.index, autopct=f"%d%%",textprops={'color':'#fff'})
    plt.show()
make_pie(strat_test_set['income_cat'].value_counts()/len(strat_test_set))

def make_pie(a): print(a.values) plt.pie(a,labels=a.index, autopct=f"%d%%",textprops={'color':'#fff'}) plt.show() make_pie(strat_test_set['income_cat'].value_counts()/len(strat_test_set))

[0.35053295 0.31879845 0.17635659 0.11434109 0.03997093]

In [ ]:

# housing['scaled_population'] = housing['population'].apply(lambda x: x/100)
housing.plot(kind="scatter",x='longitude',y='latitude',alpha=.4,
             figsize= (10,7),
             s=housing['population']/100, label="population",
             c="median_house_value", colorbar=True, cmap=plt.get_cmap('jet'))
# housing.drop('scaled_population',axis=1)
plt.show()

# housing['scaled_population'] = housing['population'].apply(lambda x: x/100) housing.plot(kind="scatter",x='longitude',y='latitude',alpha=.4, figsize= (10,7), s=housing['population']/100, label="population", c="median_house_value", colorbar=True, cmap=plt.get_cmap('jet')) # housing.drop('scaled_population',axis=1) plt.show()

In [ ]:

from pandas.plotting import scatter_matrix
# scatter_plot = scatter_matrix(housing) # bad idea: too many variables
attributes = ["median_house_value", "median_income", "total_rooms", "housing_median_age"]
scatter_matrix(housing[attributes],figsize=(12,8))
plt.show()

from pandas.plotting import scatter_matrix # scatter_plot = scatter_matrix(housing) # bad idea: too many variables attributes = ["median_house_value", "median_income", "total_rooms", "housing_median_age"] scatter_matrix(housing[attributes],figsize=(12,8)) plt.show()

In [ ]:

housing.plot(kind="scatter", x="median_income",y="median_house_value",
            alpha=0.1,)
plt.show()

housing.plot(kind="scatter", x="median_income",y="median_house_value", alpha=0.1,) plt.show()

Prepare Data¶

• Derived features (e.g. Population per household)
• Missing Values
  • Omit
  • Impute
• Encode (categorical variables)
• Define the $y$ and $X$ and apply the transformation to them

One mistake that you did was that you are using indices later ix but you are not using the indices of the raw data because the raw data was altered during the exploration phase.

housing['rooms_per_household'] = housing['total_rooms'] / housing['households']
housing['bedrooms_per_room'] = housing['total_bedrooms'] / housing['total_rooms']
housing['population_per_household'] = housing['population'] / housing['households']

In [ ]:

# 1. get rid of the corresponding districts
housing_clean = housing.dropna(subset=['total_bedrooms'])
# 2. get rid of the entire column
housing_clean = housing.drop('total_bedrooms', axis=1)

# 1. get rid of the corresponding districts housing_clean = housing.dropna(subset=['total_bedrooms']) # 2. get rid of the entire column housing_clean = housing.drop('total_bedrooms', axis=1)

(Manually) Impute & Encode¶

• Impute using a relavant strategy
• Encode
  • Ordinal (when there is inherent order, e.g. typical survey responses)
  • One-hot encode (when there is no inherent order, ocean proximity)

In [ ]:

# 3. replace it with some value (e.g. mean/median)
median = housing['total_bedrooms'].median()

# 4. REMOVE IT ALTOGETHER
# housing['total_bedrooms'].fillna(median, inplace=True)
# Use this from pandas 3.0
# housing.fillna({'total_bedrooms': median}, inplace=True)

# 3. replace it with some value (e.g. mean/median) median = housing['total_bedrooms'].median() # 4. REMOVE IT ALTOGETHER # housing['total_bedrooms'].fillna(median, inplace=True) # Use this from pandas 3.0 # housing.fillna({'total_bedrooms': median}, inplace=True)

or use SimpleImputer

In [ ]:

from sklearn.impute import SimpleImputer

imputer = SimpleImputer(strategy='median')

# Only numerical values
housing_num = housing.drop('ocean_proximity', axis=1)
imputer.fit(housing_num)
X_train_num = imputer.transform(housing_num)
housing_tr = pd.DataFrame(X_train_num, columns=housing_num.columns, index= housing_num.index)

# ========== MISC ===========
# imputer.strategy
# imputer.statistics_
# housing_num.median().values
# ===========================

from sklearn.impute import SimpleImputer imputer = SimpleImputer(strategy='median') # Only numerical values housing_num = housing.drop('ocean_proximity', axis=1) imputer.fit(housing_num) X_train_num = imputer.transform(housing_num) housing_tr = pd.DataFrame(X_train_num, columns=housing_num.columns, index= housing_num.index) # ========== MISC =========== # imputer.strategy # imputer.statistics_ # housing_num.median().values # ===========================

OrdinalEncoder

In [ ]:

housing_cat = housing[['ocean_proximity']]

from sklearn.preprocessing import OrdinalEncoder
ordinal_encoder = OrdinalEncoder()
housing_cat_encoded = ordinal_encoder.fit_transform(housing_cat)

# ========== MISC ===========
# ordinal_encoder.categories_
# ===========================

housing_cat_encoded # array

housing_cat = housing[['ocean_proximity']] from sklearn.preprocessing import OrdinalEncoder ordinal_encoder = OrdinalEncoder() housing_cat_encoded = ordinal_encoder.fit_transform(housing_cat) # ========== MISC =========== # ordinal_encoder.categories_ # =========================== housing_cat_encoded # array

Out[ ]:

array([[3.],
       [3.],
       [3.],
       ...,
       [1.],
       [1.],
       [1.]])

OneHotEncoder

In [ ]:

from sklearn.preprocessing import OneHotEncoder

cat_encoder = OneHotEncoder()
cat_encoder.fit(housing_cat)
housing_cat_1hot = cat_encoder.transform(housing_cat)


# ========== MISC ===========
# housing_cat_1hot.toarray() # back to numpy array
# cat_encoder.categories_
# ===========================

housing_cat_1hot # SciPy sparse matrix (capable of handling categories with 1000s of levels, numpy array would bonk)

from sklearn.preprocessing import OneHotEncoder cat_encoder = OneHotEncoder() cat_encoder.fit(housing_cat) housing_cat_1hot = cat_encoder.transform(housing_cat) # ========== MISC =========== # housing_cat_1hot.toarray() # back to numpy array # cat_encoder.categories_ # =========================== housing_cat_1hot # SciPy sparse matrix (capable of handling categories with 1000s of levels, numpy array would bonk)

Out[ ]:

<Compressed Sparse Row sparse matrix of dtype 'float64'
	with 20640 stored elements and shape (20640, 5)>

Transformers & Pipelines¶

• Write a transformer to apply all the data cleaning steps (defined above) at once.
• Pipeline to impute, transform and scale at once

In [ ]:

from sklearn.base import BaseEstimator, TransformerMixin

housing_num = housing.drop(['ocean_proximity'], axis=1)
rooms_ix, bedrooms_ix, population_ix, households_ix, value_ix  = 3, 4, 5, 6, 8
housing_columns = housing.columns.delete(value_ix)

class LabelRemover(BaseEstimator, TransformerMixin):
    def __init__(self, label_name = 'median_house_value'):
        self.label_name = label_name

    def fit(self, X, y=None):
        return self

    def transform(self, X: pd.DataFrame):
        X = X.drop(self.label_name, axis=1)
        return X


class CombinedAttributesAdder(BaseEstimator, TransformerMixin):
    '''
    This transforms e raw data `housing` to `X` (drops )
    '''
    def __init__(self, add_bedrooms_per_room = True):
        self.add_bedrooms_per_room = add_bedrooms_per_room

    def fit(self, X, y=None):
        return self

    def transform(self, X):
        rooms_per_household = X[:, rooms_ix] / X[:, households_ix]
        population_per_household = X[:, population_ix] / X[:, households_ix]
        if self.add_bedrooms_per_room:
            bedrooms_per_room = X[:, bedrooms_ix] / X[:, rooms_ix]
            self.column_names_added_ = ['rooms_per_household', 'population_per_household', 'bedrooms_per_room']
            return np.c_[X, rooms_per_household, population_per_household, bedrooms_per_room]
        else:
            self.column_names_added_ = ['rooms_per_household', 'population_per_household']
            return np.c_[X, rooms_per_household, population_per_household]

from sklearn.base import BaseEstimator, TransformerMixin housing_num = housing.drop(['ocean_proximity'], axis=1) rooms_ix, bedrooms_ix, population_ix, households_ix, value_ix = 3, 4, 5, 6, 8 housing_columns = housing.columns.delete(value_ix) class LabelRemover(BaseEstimator, TransformerMixin): def __init__(self, label_name = 'median_house_value'): self.label_name = label_name def fit(self, X, y=None): return self def transform(self, X: pd.DataFrame): X = X.drop(self.label_name, axis=1) return X class CombinedAttributesAdder(BaseEstimator, TransformerMixin): ''' This transforms e raw data `housing` to `X` (drops ) ''' def __init__(self, add_bedrooms_per_room = True): self.add_bedrooms_per_room = add_bedrooms_per_room def fit(self, X, y=None): return self def transform(self, X): rooms_per_household = X[:, rooms_ix] / X[:, households_ix] population_per_household = X[:, population_ix] / X[:, households_ix] if self.add_bedrooms_per_room: bedrooms_per_room = X[:, bedrooms_ix] / X[:, rooms_ix] self.column_names_added_ = ['rooms_per_household', 'population_per_household', 'bedrooms_per_room'] return np.c_[X, rooms_per_household, population_per_household, bedrooms_per_room] else: self.column_names_added_ = ['rooms_per_household', 'population_per_household'] return np.c_[X, rooms_per_household, population_per_household]

In [ ]:

attr_adder = CombinedAttributesAdder(add_bedrooms_per_room=False)
housing_extra_attribs = attr_adder.transform(housing.values)
housing_add = pd.DataFrame(housing_extra_attribs , columns=list(housing.columns)+attr_adder.column_names_added_, index=housing.index)
housing_add.shape # 11 + 2 (columns added)

attr_adder = CombinedAttributesAdder(add_bedrooms_per_room=False) housing_extra_attribs = attr_adder.transform(housing.values) housing_add = pd.DataFrame(housing_extra_attribs , columns=list(housing.columns)+attr_adder.column_names_added_, index=housing.index) housing_add.shape # 11 + 2 (columns added)

Out[ ]:

(20640, 13)

Creating the Pipeline

• Numerical pipeline
• Combine Numerical & Categorical columns using ColumnTransformer

In [ ]:

from sklearn.preprocessing import StandardScaler
from sklearn.pipeline import Pipeline

num_pipeline = Pipeline([
    ('label_remover', LabelRemover()),
    ('imputer', SimpleImputer(strategy='median')),
    ('attrib_adder', CombinedAttributesAdder(add_bedrooms_per_room=False)),
    ('std_scaler', StandardScaler())
])

from sklearn.preprocessing import StandardScaler from sklearn.pipeline import Pipeline num_pipeline = Pipeline([ ('label_remover', LabelRemover()), ('imputer', SimpleImputer(strategy='median')), ('attrib_adder', CombinedAttributesAdder(add_bedrooms_per_room=False)), ('std_scaler', StandardScaler()) ])

In [ ]:

housing_num_tr = num_pipeline.fit_transform(housing_num)
num_pipeline.named_steps['attrib_adder']
housing_piped = pd.DataFrame(housing_num_tr, columns=list(housing_num.columns.delete(value_ix))+num_pipeline.named_steps['attrib_adder'].column_names_added_, index= housing_num.index)

housing_piped.shape

housing_num_tr = num_pipeline.fit_transform(housing_num) num_pipeline.named_steps['attrib_adder'] housing_piped = pd.DataFrame(housing_num_tr, columns=list(housing_num.columns.delete(value_ix))+num_pipeline.named_steps['attrib_adder'].column_names_added_, index= housing_num.index) housing_piped.shape

Out[ ]:

(20640, 11)

In [ ]:

from sklearn.compose import ColumnTransformer

num_attribs = list(housing_num)
cat_attribs = ['ocean_proximity']


full_pipeline = ColumnTransformer([
    ('num', num_pipeline, num_attribs),
    ('cat', OneHotEncoder(), cat_attribs)
])

from sklearn.compose import ColumnTransformer num_attribs = list(housing_num) cat_attribs = ['ocean_proximity'] full_pipeline = ColumnTransformer([ ('num', num_pipeline, num_attribs), ('cat', OneHotEncoder(), cat_attribs) ])

In [ ]:

X_train = full_pipeline.fit_transform(strat_train_set)
y_train = strat_train_set['median_house_value'].copy()


# ========== MISC ===========
# How to access specific steps from the transformer?
# full_pipeline.named_transformers_['num'].named_steps['attrib_adder'].column_names_added_
# ===========================

X_train = full_pipeline.fit_transform(strat_train_set) y_train = strat_train_set['median_house_value'].copy() # ========== MISC =========== # How to access specific steps from the transformer? # full_pipeline.named_transformers_['num'].named_steps['attrib_adder'].column_names_added_ # ===========================

Select & Train¶

Note: $y$ represents Labels

• Be comfortable applying transformations to any data
• Decide and run the model
  • Linear Regression
  • Decision Tree Regressor
  • Random Forest Regressor
• For each model
  • root mean_squared_error (rmse)
  • Cross Validation

LinearRegression

In [ ]:

from sklearn.linear_model import LinearRegression

lin_reg = LinearRegression()
lin_reg.fit(X_train, y_train)

from sklearn.linear_model import LinearRegression lin_reg = LinearRegression() lin_reg.fit(X_train, y_train)

Out[ ]:

LinearRegression()

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Apply transformations to data and performing predictions from a model:

In [ ]:

# Data
some_data = strat_train_set.iloc[:5]
some_labels = y_train.iloc[:5]

# Transform
some_data_prepared = full_pipeline.transform(some_data)

some_predictions = lin_reg.predict(some_data_prepared)
pd.DataFrame(np.c_[some_labels, some_predictions], columns=['Labels (actual)', 'Predictions'], index= some_data.index)

# Data some_data = strat_train_set.iloc[:5] some_labels = y_train.iloc[:5] # Transform some_data_prepared = full_pipeline.transform(some_data) some_predictions = lin_reg.predict(some_data_prepared) pd.DataFrame(np.c_[some_labels, some_predictions], columns=['Labels (actual)', 'Predictions'], index= some_data.index)

Out[ ]:

	Labels (actual)	Predictions
12655	72100.0	87112.228684
15502	279600.0	311287.678951
2908	82700.0	149138.756997
14053	112500.0	181605.044171
20496	238300.0	242876.060794

Model Metrics¶

• Root mean squared error
• TIP: Make reusable functions for this

In [ ]:

from sklearn.metrics import mean_squared_error

y_pred = lin_reg.predict(X_train)
lin_mse = mean_squared_error(y_train, y_pred)
lin_rmse = np.sqrt(lin_mse)
lin_rmse

from sklearn.metrics import mean_squared_error y_pred = lin_reg.predict(X_train) lin_mse = mean_squared_error(y_train, y_pred) lin_rmse = np.sqrt(lin_mse) lin_rmse

Out[ ]:

np.float64(68740.3505914331)

DecisionTreeRegressor

In [ ]:

from sklearn.tree import DecisionTreeRegressor

tree_reg = DecisionTreeRegressor()
tree_reg.fit(X_train, y_train)

from sklearn.tree import DecisionTreeRegressor tree_reg = DecisionTreeRegressor() tree_reg.fit(X_train, y_train)

Out[ ]:

DecisionTreeRegressor()

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In [ ]:

y_pred = tree_reg.predict(X_train)

tree_mse = mean_squared_error(y_train, y_pred)
tree_rmse = np.sqrt(tree_mse)
tree_rmse
# Severely Overfitting

y_pred = tree_reg.predict(X_train) tree_mse = mean_squared_error(y_train, y_pred) tree_rmse = np.sqrt(tree_mse) tree_rmse # Severely Overfitting

Out[ ]:

np.float64(0.0)

RandomForestRegressor

In [ ]:

from sklearn.ensemble import RandomForestRegressor

# rf_reg = RandomForestRegressor()
# rf_reg.fit(X_train, y_train)

from sklearn.ensemble import RandomForestRegressor # rf_reg = RandomForestRegressor() # rf_reg.fit(X_train, y_train)

In [ ]:

# y_pred = rf_reg.predict(housing_prepared)

# rf_mse = mean_squared_error(y_train, y_pred)
# rf_rmse = np.sqrt(rf_mse)
# rf_rmse

# y_pred = rf_reg.predict(housing_prepared) # rf_mse = mean_squared_error(y_train, y_pred) # rf_rmse = np.sqrt(rf_mse) # rf_rmse

Cross Validation¶

• This expects a utility function, greater is better. (Opposite of Cost function)

• The utility function is negative, and increases as the predictive power increases

• Zero is highest

In [ ]:

from sklearn.model_selection import cross_val_score

def display_scores(scores: np.ndarray):
    print("Scores:", scores)
    print("Mean:", scores.mean())
    print("Standard Deviation", scores.std())

from sklearn.model_selection import cross_val_score def display_scores(scores: np.ndarray): print("Scores:", scores) print("Mean:", scores.mean()) print("Standard Deviation", scores.std())

In [ ]:

scores = cross_val_score(tree_reg, X_train, y_train,
                         scoring='neg_mean_squared_error', cv=10)

tree_rmse_scores = np.sqrt(-scores)
display_scores(tree_rmse_scores)

scores = cross_val_score(tree_reg, X_train, y_train, scoring='neg_mean_squared_error', cv=10) tree_rmse_scores = np.sqrt(-scores) display_scores(tree_rmse_scores)

Scores: [71237.35168416 68357.52527279 68195.75406674 72272.463544
 68562.2497614  76326.9623925  70896.87760963 70455.49034824
 68518.77870903 71425.22279391]
Mean: 70624.86761823995
Standard Deviation 2368.0852445976143

In [ ]:

scores = cross_val_score(lin_reg, X_train, y_train,
                         scoring="neg_mean_squared_error", cv=10)

lin_rmse_scores = np.sqrt(-scores)
display_scores(lin_rmse_scores)

scores = cross_val_score(lin_reg, X_train, y_train, scoring="neg_mean_squared_error", cv=10) lin_rmse_scores = np.sqrt(-scores) display_scores(lin_rmse_scores)

Scores: [72195.7979865  64520.15075203 67849.52511021 69087.47348499
 66747.91999171 72801.81116412 70339.52226055 69242.33276019
 66692.04641613 70356.66638109]
Mean: 68983.3246307523
Standard Deviation 2452.682376861631

In [ ]:

# # DON'T RUN. TAKES TOO MUCH TIME!!
# scores = cross_val_score(rf_reg, X_train, y_train
#                          scoring="neg_mean_squared_error", cv=10)

# rf_rmse_scores = np.sqrt(-scores)
# display_scores(rf_rmse_scores)

# # DON'T RUN. TAKES TOO MUCH TIME!! # scores = cross_val_score(rf_reg, X_train, y_train # scoring="neg_mean_squared_error", cv=10) # rf_rmse_scores = np.sqrt(-scores) # display_scores(rf_rmse_scores)

Fine-Tune¶

• Grid search CV
• Feature importances

In [ ]:

from sklearn.model_selection import GridSearchCV

param_grid = [
    {'n_estimators': [3, 10, 30], 'max_features': [2, 4, 6, 8]},
    {'bootstrap': [False], 'n_estimators': [3, 10], 'max_features': [2, 3, 4]}
]

forest_reg = RandomForestRegressor()

grid_search = GridSearchCV(forest_reg, param_grid, cv=5,
                           scoring="neg_mean_squared_error",
                           return_train_score=True)

grid_search.fit(X_train, y_train)

# ========== MISC ===========
# grid_search.best_params_
# grid_search.best_estimator_
# ===========================

from sklearn.model_selection import GridSearchCV param_grid = [ {'n_estimators': [3, 10, 30], 'max_features': [2, 4, 6, 8]}, {'bootstrap': [False], 'n_estimators': [3, 10], 'max_features': [2, 3, 4]} ] forest_reg = RandomForestRegressor() grid_search = GridSearchCV(forest_reg, param_grid, cv=5, scoring="neg_mean_squared_error", return_train_score=True) grid_search.fit(X_train, y_train) # ========== MISC =========== # grid_search.best_params_ # grid_search.best_estimator_ # ===========================

Out[ ]:

GridSearchCV(cv=5, estimator=RandomForestRegressor(),
             param_grid=[{'max_features': [2, 4, 6, 8],
                          'n_estimators': [3, 10, 30]},
                         {'bootstrap': [False], 'max_features': [2, 3, 4],
                          'n_estimators': [3, 10]}],
             return_train_score=True, scoring='neg_mean_squared_error')

In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
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View Results and determine important features

In [ ]:

cvres = grid_search.cv_results_
for mean_score, params in zip(cvres['mean_test_score'], cvres["params"]):
    print(np.sqrt(-mean_score), params)

cvres = grid_search.cv_results_ for mean_score, params in zip(cvres['mean_test_score'], cvres["params"]): print(np.sqrt(-mean_score), params)

63063.86122253507 {'max_features': 2, 'n_estimators': 3}
55156.84196984595 {'max_features': 2, 'n_estimators': 10}
52905.0930835154 {'max_features': 2, 'n_estimators': 30}
60784.62307278264 {'max_features': 4, 'n_estimators': 3}
53524.10514849476 {'max_features': 4, 'n_estimators': 10}
50719.11786462384 {'max_features': 4, 'n_estimators': 30}
59942.50730403312 {'max_features': 6, 'n_estimators': 3}
52887.13885955089 {'max_features': 6, 'n_estimators': 10}
50711.59935939004 {'max_features': 6, 'n_estimators': 30}
59081.19617320614 {'max_features': 8, 'n_estimators': 3}
51880.750719423966 {'max_features': 8, 'n_estimators': 10}
50625.93683919922 {'max_features': 8, 'n_estimators': 30}
62169.33046545155 {'bootstrap': False, 'max_features': 2, 'n_estimators': 3}
54476.43461087879 {'bootstrap': False, 'max_features': 2, 'n_estimators': 10}
59809.79004268255 {'bootstrap': False, 'max_features': 3, 'n_estimators': 3}
52925.59007801043 {'bootstrap': False, 'max_features': 3, 'n_estimators': 10}
58748.690886767734 {'bootstrap': False, 'max_features': 4, 'n_estimators': 3}
52253.09709961589 {'bootstrap': False, 'max_features': 4, 'n_estimators': 10}

In [ ]:

feature_importances = grid_search.best_estimator_.feature_importances_

# ...num_attribs already defined above...
extra_attribs = full_pipeline.named_transformers_['num'].named_steps['attrib_adder'].column_names_added_
cat_one_hot_attribs = list(
    full_pipeline.named_transformers_['cat'] .categories_[0] )

attributes = num_attribs + extra_attribs + cat_one_hot_attribs
sorted(zip([f"{x:5.5f}" for x in feature_importances], attributes), reverse=True)

feature_importances = grid_search.best_estimator_.feature_importances_ # ...num_attribs already defined above... extra_attribs = full_pipeline.named_transformers_['num'].named_steps['attrib_adder'].column_names_added_ cat_one_hot_attribs = list( full_pipeline.named_transformers_['cat'] .categories_[0] ) attributes = num_attribs + extra_attribs + cat_one_hot_attribs sorted(zip([f"{x:5.5f}" for x in feature_importances], attributes), reverse=True)

Out[ ]:

[('0.35517', 'median_income'),
 ('0.12696', '<1H OCEAN'),
 ('0.11552', 'rooms_per_household'),
 ('0.11141', 'median_house_value'),
 ('0.06340', 'longitude'),
 ('0.05956', 'latitude'),
 ('0.04892', 'housing_median_age'),
 ('0.04060', 'income_cat'),
 ('0.01746', 'population'),
 ('0.01723', 'total_bedrooms'),
 ('0.01695', 'total_rooms'),
 ('0.01686', 'households'),
 ('0.00456', 'population_per_household'),
 ('0.00295', 'NEAR BAY'),
 ('0.00236', 'ISLAND'),
 ('0.00008', 'INLAND')]

Final Model¶

• Incorporate the best parameters.
• The conclusion or takeaway of this analysis

In [ ]:

final_model = grid_search.best_estimator_

X_test = full_pipeline.transform(strat_test_set)
y_test = strat_test_set['median_house_value'].copy()

final_predictions = final_model.predict(X_test)
final_rmse = np.sqrt(mean_squared_error(y_test, final_predictions))
print(f'RSME: {final_rmse}')

final_model = grid_search.best_estimator_ X_test = full_pipeline.transform(strat_test_set) y_test = strat_test_set['median_house_value'].copy() final_predictions = final_model.predict(X_test) final_rmse = np.sqrt(mean_squared_error(y_test, final_predictions)) print(f'RSME: {final_rmse}')

RSME: 48901.503020024174

Save & Load¶

In [ ]:

import joblib

# save
joblib.dump(final_model, "housing_final.pkl")

# load
final_model_loaded = joblib.load('housing_final.pkl')

import joblib # save joblib.dump(final_model, "housing_final.pkl") # load final_model_loaded = joblib.load('housing_final.pkl')

In [ ]:

housing_test = full_pipeline.transform(housing.iloc[1000:2000])
housing_test.shape

final_model_loaded.predict(housing_test)[:10]

housing_test = full_pipeline.transform(housing.iloc[1000:2000]) housing_test.shape final_model_loaded.predict(housing_test)[:10]

Out[ ]:

array([162076.66666667, 157296.66666667, 134113.33333333, 160950.        ,
       188446.66666667, 244736.66666667, 175210.        , 216380.03333333,
       166066.66666667, 174626.66666667])