Python 3.7.6 (default, Jan 8 2020, 20:23:39) [MSC v.1916 64 bit (AMD64)]
Type "copyright", "credits" or "license" for more information.
IPython 7.12.0 -- An enhanced Interactive Python.
import pandas as pd
import numpy as np
housing = pd.read_csv(r"G:\机器学习实战：基于Scikit-Learn和TensorFlow\housing.csv")
housing.head()
Out[1]:
longitude latitude ... median_house_value ocean_proximity
0 -122.23 37.88 ... 452600.0 NEAR BAY
1 -122.22 37.86 ... 358500.0 NEAR BAY
2 -122.24 37.85 ... 352100.0 NEAR BAY
3 -122.25 37.85 ... 341300.0 NEAR BAY
4 -122.25 37.85 ... 342200.0 NEAR BAY
[5 rows x 10 columns]
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
housing["ocean_proximity"].value_counts()
Out[3]:
<1H OCEAN 9136
INLAND 6551
NEAR OCEAN 2658
NEAR BAY 2290
ISLAND 5
Name: ocean_proximity, dtype: int64
housing.describe()
Out[4]:
longitude latitude ... median_income median_house_value
count 20640.000000 20640.000000 ... 20640.000000 20640.000000
mean -119.569704 35.631861 ... 3.870671 206855.816909
std 2.003532 2.135952 ... 1.899822 115395.615874
min -124.350000 32.540000 ... 0.499900 14999.000000
25% -121.800000 33.930000 ... 2.563400 119600.000000
50% -118.490000 34.260000 ... 3.534800 179700.000000
75% -118.010000 37.710000 ... 4.743250 264725.000000
max -114.310000 41.950000 ... 15.000100 500001.000000
[8 rows x 9 columns]
import matplotlib.pyplot as plt
housing.hist(bins=50, figsize=(20,15))
plt.show()
from sklearn.model_selection import train_test_split
train_set, test_set = train_test_split(housing, test_size=0.2, random_state=42)
housing["median_income"].hist()
Out[7]: <matplotlib.axes._subplots.AxesSubplot at 0x194e15d21c8>
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"].value_counts()
Out[9]:
3 7236
2 6581
4 3639
5 2362
1 822
Name: income_cat, dtype: int64
housing["income_cat"].hist()
Out[10]: <matplotlib.axes._subplots.AxesSubplot at 0x194e2c776c8>
from sklearn.model_selection import StratifiedShuffleSplit
split = StratifiedShuffleSplit(n_splits=1, test_size=0.2, random_state=42)
for train_index, test_index in split.split(housing, housing["income_cat"]):
strat_train_set = housing.loc[train_index]
strat_test_set = housing.loc[test_index]
strat_test_set["income_cat"].value_counts() / len(strat_test_set)
Out[12]:
3 0.350533
2 0.318798
4 0.176357
5 0.114583
1 0.039729
Name: income_cat, dtype: float64
housing["income_cat"].value_counts() / len(housing)
Out[13]:
3 0.350581
2 0.318847
4 0.176308
5 0.114438
1 0.039826
Name: income_cat, dtype: float64
def income_cat_proportions(data):
return data["income_cat"].value_counts() / len(data)
train_set, test_set = train_test_split(housing, test_size=0.2, random_state=42)
compare_props = pd.DataFrame({
"Overall": income_cat_proportions(housing),
"Stratified": income_cat_proportions(strat_test_set),
"Random": income_cat_proportions(test_set),
}).sort_index()
compare_props["Rand. %error"] = 100 * compare_props["Random"] / compare_props["Overall"] - 100
compare_props["Strat. %error"] = 100 * compare_props["Stratified"] / compare_props["Overall"] - 100
compare_props
Out[16]:
Overall Stratified Random Rand. %error Strat. %error
1 0.039826 0.039729 0.040213 0.973236 -0.243309
2 0.318847 0.318798 0.324370 1.732260 -0.015195
3 0.350581 0.350533 0.358527 2.266446 -0.013820
4 0.176308 0.176357 0.167393 -5.056334 0.027480
5 0.114438 0.114583 0.109496 -4.318374 0.127011
for set_ in (strat_train_set, strat_test_set):
set_.drop("income_cat", axis=1, inplace=True)
housing = strat_train_set.copy()
housing.plot(kind="scatter", x="longitude", y="latitude")
Out[19]: <matplotlib.axes._subplots.AxesSubplot at 0x194e193bd88>
housing.plot(kind="scatter", x="longitude", y="latitude", alpha=0.1)
Out[20]: <matplotlib.axes._subplots.AxesSubplot at 0x194e19b0b48>
housing.plot(kind="scatter", x="longitude", y="latitude", alpha=0.4,
s=housing["population"]/100, label="population", figsize=(10,7),
c="median_house_value", cmap=plt.get_cmap("jet"), colorbar=True,
sharex=False)
plt.legend()
Out[21]: <matplotlib.legend.Legend at 0x194e18da108>
corr_matrix = housing.corr()
corr_matrix["median_house_value"].sort_values(ascending=False)
Out[22]:
median_house_value 1.000000
median_income 0.687160
total_rooms 0.135097
housing_median_age 0.114110
households 0.064506
total_bedrooms 0.047689
population -0.026920
longitude -0.047432
latitude -0.142724
Name: median_house_value, dtype: float64
from pandas.plotting import scatter_matrix
attributes = ["median_house_value", "median_income", "total_rooms",
"housing_median_age"]
scatter_matrix(housing[attributes], figsize=(12, 8))
Out[23]:
array([[<matplotlib.axes._subplots.AxesSubplot object at 0x00000194E46F6608>,
<matplotlib.axes._subplots.AxesSubplot object at 0x00000194E470E988>,
<matplotlib.axes._subplots.AxesSubplot object at 0x00000194E4868F08>,
<matplotlib.axes._subplots.AxesSubplot object at 0x00000194E48958C8>],
[<matplotlib.axes._subplots.AxesSubplot object at 0x00000194E48C5288>,
<matplotlib.axes._subplots.AxesSubplot object at 0x00000194E48F9388>,
<matplotlib.axes._subplots.AxesSubplot object at 0x00000194E49A2488>,
<matplotlib.axes._subplots.AxesSubplot object at 0x00000194E49D9588>],
[<matplotlib.axes._subplots.AxesSubplot object at 0x00000194E49E7188>,
<matplotlib.axes._subplots.AxesSubplot object at 0x00000194E4A1E348>,
<matplotlib.axes._subplots.AxesSubplot object at 0x00000194E4BE2908>,
<matplotlib.axes._subplots.AxesSubplot object at 0x00000194E4C1A6C8>],
[<matplotlib.axes._subplots.AxesSubplot object at 0x00000194E4C4F848>,
<matplotlib.axes._subplots.AxesSubplot object at 0x00000194E4C87988>,
<matplotlib.axes._subplots.AxesSubplot object at 0x00000194E4CC1A88>,
<matplotlib.axes._subplots.AxesSubplot object at 0x00000194E4CFABC8>]],
dtype=object)
housing.plot(kind="scatter", x="median_income", y="median_house_value",
alpha=0.1)
plt.axis([0, 16, 0, 550000])
Out[24]: [0, 16, 0, 550000]
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"]
corr_matrix = housing.corr()
corr_matrix["median_house_value"].sort_values(ascending=False)
Out[29]:
median_house_value 1.000000
median_income 0.687160
rooms_per_household 0.146285
total_rooms 0.135097
housing_median_age 0.114110
households 0.064506
total_bedrooms 0.047689
population_per_household -0.021985
population -0.026920
longitude -0.047432
latitude -0.142724
bedrooms_per_room -0.259984
Name: median_house_value, dtype: float64
housing.plot(kind="scatter", x="rooms_per_household", y="median_house_value",
alpha=0.2)
plt.axis([0, 5, 0, 520000])
Out[30]: [0, 5, 0, 520000]
housing.describe()
Out[31]:
longitude latitude ... bedrooms_per_room population_per_household
count 16512.000000 16512.000000 ... 16354.000000 16512.000000
mean -119.575834 35.639577 ... 0.212878 3.096437
std 2.001860 2.138058 ... 0.057379 11.584826
min -124.350000 32.540000 ... 0.100000 0.692308
25% -121.800000 33.940000 ... 0.175304 2.431287
50% -118.510000 34.260000 ... 0.203031 2.817653
75% -118.010000 37.720000 ... 0.239831 3.281420
max -114.310000 41.950000 ... 1.000000 1243.333333
[8 rows x 12 columns]
housing = strat_train_set.drop("median_house_value", axis=1) # drop labels for training set
housing_labels = strat_train_set["median_house_value"].copy()
sample_incomplete_rows = housing[housing.isnull().any(axis=1)].head()
sample_incomplete_rows
Out[33]:
longitude latitude ... median_income ocean_proximity
4629 -118.30 34.07 ... 2.2708 <1H OCEAN
6068 -117.86 34.01 ... 5.1762 <1H OCEAN
17923 -121.97 37.35 ... 4.6328 <1H OCEAN
13656 -117.30 34.05 ... 1.6675 INLAND
19252 -122.79 38.48 ... 3.1662 <1H OCEAN
[5 rows x 9 columns]
sample_incomplete_rows.dropna(subset=["total_bedrooms"])
Out[34]:
Empty DataFrame
Columns: [longitude, latitude, housing_median_age, total_rooms, total_bedrooms, population, households, median_income, ocean_proximity]
Index: []
sample_incomplete_rows.drop("total_bedrooms", axis=1)
Out[35]:
longitude latitude ... median_income ocean_proximity
4629 -118.30 34.07 ... 2.2708 <1H OCEAN
6068 -117.86 34.01 ... 5.1762 <1H OCEAN
17923 -121.97 37.35 ... 4.6328 <1H OCEAN
13656 -117.30 34.05 ... 1.6675 INLAND
19252 -122.79 38.48 ... 3.1662 <1H OCEAN
[5 rows x 8 columns]
median = housing["total_bedrooms"].median()
sample_incomplete_rows["total_bedrooms"].fillna(median, inplace=True) # option 3
sample_incomplete_rows
Out[36]:
longitude latitude ... median_income ocean_proximity
4629 -118.30 34.07 ... 2.2708 <1H OCEAN
6068 -117.86 34.01 ... 5.1762 <1H OCEAN
17923 -121.97 37.35 ... 4.6328 <1H OCEAN
13656 -117.30 34.05 ... 1.6675 INLAND
19252 -122.79 38.48 ... 3.1662 <1H OCEAN
[5 rows x 9 columns]
try:
from sklearn.impute import SimpleImputer # Scikit-Learn 0.20+
except ImportError:
from sklearn.preprocessing import Imputer as SimpleImputer
imputer = SimpleImputer(strategy="median")
housing_num = housing.drop('ocean_proximity', axis=1)
imputer.fit(housing_num)
imputer.statistics_
Out[39]:
array([-118.51 , 34.26 , 29. , 2119.5 , 433. , 1164. ,
408. , 3.5409])
housing_num.median().values
Out[40]:
array([-118.51 , 34.26 , 29. , 2119.5 , 433. , 1164. ,
408. , 3.5409])
X = imputer.transform(housing_num)
housing_tr = pd.DataFrame(X, columns=housing_num.columns,
index=housing.index)
housing_tr.loc[sample_incomplete_rows.index.values]
Out[41]:
longitude latitude ... households median_income
4629 -118.30 34.07 ... 1462.0 2.2708
6068 -117.86 34.01 ... 727.0 5.1762
17923 -121.97 37.35 ... 386.0 4.6328
13656 -117.30 34.05 ... 391.0 1.6675
19252 -122.79 38.48 ... 1405.0 3.1662
[5 rows x 8 columns]
housing_tr = pd.DataFrame(X, columns=housing_num.columns,
index=housing_num.index)
housing_tr.head()
Out[42]:
longitude latitude ... households median_income
17606 -121.89 37.29 ... 339.0 2.7042
18632 -121.93 37.05 ... 113.0 6.4214
14650 -117.20 32.77 ... 462.0 2.8621
3230 -119.61 36.31 ... 353.0 1.8839
3555 -118.59 34.23 ... 1463.0 3.0347
[5 rows x 8 columns]
housing_cat = housing[['ocean_proximity']]
housing_cat.head(10)
Out[43]:
ocean_proximity
17606 <1H OCEAN
18632 <1H OCEAN
14650 NEAR OCEAN
3230 INLAND
3555 <1H OCEAN
19480 INLAND
8879 <1H OCEAN
13685 INLAND
4937 <1H OCEAN
4861 <1H OCEAN
try:
from sklearn.preprocessing import OrdinalEncoder
except ImportError:
from future_encoders import OrdinalEncoder # Scikit-Learn < 0.20
ordinal_encoder = OrdinalEncoder()
housing_cat_encoded = ordinal_encoder.fit_transform(housing_cat)
housing_cat_encoded[:10]
Out[44]:
array([[0.],
[0.],
[4.],
[1.],
[0.],
[1.],
[0.],
[1.],
[0.],
[0.]])
ordinal_encoder.categories_
Out[45]:
[array(['<1H OCEAN', 'INLAND', 'ISLAND', 'NEAR BAY', 'NEAR OCEAN'],
dtype=object)]
try:
from sklearn.preprocessing import OrdinalEncoder # just to raise an ImportError if Scikit-Learn < 0.20
from sklearn.preprocessing import OneHotEncoder
except ImportError:
from future_encoders import OneHotEncoder
cat_encoder = OneHotEncoder()
housing_cat_1hot = cat_encoder.fit_transform(housing_cat)
housing_cat_1hot
Out[47]:
<16512x5 sparse matrix of type '<class 'numpy.float64'>'
with 16512 stored elements in Compressed Sparse Row format>
housing_cat_1hot.toarray()
Out[48]:
array([[1., 0., 0., 0., 0.],
[1., 0., 0., 0., 0.],
[0., 0., 0., 0., 1.],
...,
[0., 1., 0., 0., 0.],
[1., 0., 0., 0., 0.],
[0., 0., 0., 1., 0.]])
cat_encoder = OneHotEncoder(sparse=False)
housing_cat_1hot = cat_encoder.fit_transform(housing_cat)
housing_cat_1hot
Out[49]:
array([[1., 0., 0., 0., 0.],
[1., 0., 0., 0., 0.],
[0., 0., 0., 0., 1.],
...,
[0., 1., 0., 0., 0.],
[1., 0., 0., 0., 0.],
[0., 0., 0., 1., 0.]])
cat_encoder.categories_
Out[50]:
[array(['<1H OCEAN', 'INLAND', 'ISLAND', 'NEAR BAY', 'NEAR OCEAN'],
dtype=object)]
housing.columns
Out[51]:
Index(['longitude', 'latitude', 'housing_median_age', 'total_rooms',
'total_bedrooms', 'population', 'households', 'median_income',
'ocean_proximity'],
dtype='object')
from sklearn.base import BaseEstimator, TransformerMixin
rooms_ix, bedrooms_ix, population_ix, household_ix = [
list(housing.columns).index(col)
for col in ("total_rooms", "total_bedrooms", "population", "households")]
class CombinedAttributesAdder(BaseEstimator, TransformerMixin):
def __init__(self, add_bedrooms_per_room = True): # no *args or **kwargs
self.add_bedrooms_per_room = add_bedrooms_per_room
def fit(self, X, y=None):
return self # nothing else to do
def transform(self, X, y=None):
rooms_per_household = X[:, rooms_ix] / X[:, household_ix]
population_per_household = X[:, population_ix] / X[:, household_ix]
if self.add_bedrooms_per_room:
bedrooms_per_room = X[:, bedrooms_ix] / X[:, rooms_ix]
return np.c_[X, rooms_per_household, population_per_household,
bedrooms_per_room]
else:
return np.c_[X, rooms_per_household, population_per_household]
attr_adder = CombinedAttributesAdder(add_bedrooms_per_room=False)
housing_extra_attribs = attr_adder.transform(housing.values)
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
num_pipeline = Pipeline([
('imputer', SimpleImputer(strategy = "median")),
('attribs_adder', CombinedAttributesAdder()),
('standard_scaler', StandardScaler()),
])
housing_num_tr = num_pipeline.fit_transform(housing_num)
housing_num_tr
Out[58]:
array([[-1.15604281, 0.77194962, 0.74333089, ..., -0.31205452,
-0.08649871, 0.15531753],
[-1.17602483, 0.6596948 , -1.1653172 , ..., 0.21768338,
-0.03353391, -0.83628902],
[ 1.18684903, -1.34218285, 0.18664186, ..., -0.46531516,
-0.09240499, 0.4222004 ],
...,
[ 1.58648943, -0.72478134, -1.56295222, ..., 0.3469342 ,
-0.03055414, -0.52177644],
[ 0.78221312, -0.85106801, 0.18664186, ..., 0.02499488,
0.06150916, -0.30340741],
[-1.43579109, 0.99645926, 1.85670895, ..., -0.22852947,
-0.09586294, 0.10180567]])
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),
])
housing_prepared = full_pipeline.fit_transform(housing)
housing_prepared
Out[60]:
array([[-1.15604281, 0.77194962, 0.74333089, ..., 0. ,
0. , 0. ],
[-1.17602483, 0.6596948 , -1.1653172 , ..., 0. ,
0. , 0. ],
[ 1.18684903, -1.34218285, 0.18664186, ..., 0. ,
0. , 1. ],
...,
[ 1.58648943, -0.72478134, -1.56295222, ..., 0. ,
0. , 0. ],
[ 0.78221312, -0.85106801, 0.18664186, ..., 0. ,
0. , 0. ],
[-1.43579109, 0.99645926, 1.85670895, ..., 0. ,
1. , 0. ]])
housing_prepared.shape
Out[61]: (16512, 16)
from sklearn.base import BaseEstimator, TransformerMixin
class OldDataFrameSelector(BaseEstimator, TransformerMixin):
def __init__(self, attribute_names):
self.attribute_names = attribute_names
def fit(self, X, y=None):
return self
def transform(self, X):
return X[self.attribute_names].values
from sklearn.linear_model import LinearRegression
lin_reg = LinearRegression()
lin_reg.fit(housing_prepared, housing_labels)
some_data = housing.iloc[:5]
some_labels = housing_labels.iloc[:5]
some_data_prepared = full_pipeline.transform(some_data)
print("Predictions:", lin_reg.predict(some_data_prepared))
Predictions: [210644.60459286 317768.80697211 210956.43331178 59218.98886849
189747.55849879]
print("Labels:", list(some_labels))
Labels: [286600.0, 340600.0, 196900.0, 46300.0, 254500.0]
some_data_prepared
Out[68]:
array([[-1.15604281, 0.77194962, 0.74333089, -0.49323393, -0.44543821,
-0.63621141, -0.42069842, -0.61493744, -0.31205452, -0.08649871,
0.15531753, 1. , 0. , 0. , 0. ,
0. ],
[-1.17602483, 0.6596948 , -1.1653172 , -0.90896655, -1.0369278 ,
-0.99833135, -1.02222705, 1.33645936, 0.21768338, -0.03353391,
-0.83628902, 1. , 0. , 0. , 0. ,
0. ],
[ 1.18684903, -1.34218285, 0.18664186, -0.31365989, -0.15334458,
-0.43363936, -0.0933178 , -0.5320456 , -0.46531516, -0.09240499,
0.4222004 , 0. , 0. , 0. , 0. ,
1. ],
[-0.01706767, 0.31357576, -0.29052016, -0.36276217, -0.39675594,
0.03604096, -0.38343559, -1.04556555, -0.07966124, 0.08973561,
-0.19645314, 0. , 1. , 0. , 0. ,
0. ],
[ 0.49247384, -0.65929936, -0.92673619, 1.85619316, 2.41221109,
2.72415407, 2.57097492, -0.44143679, -0.35783383, -0.00419445,
0.2699277 , 1. , 0. , 0. , 0. ,
0. ]])
from sklearn.metrics import mean_squared_error
housing_predictions = lin_reg.predict(housing_prepared)
lin_mse = mean_squared_error(housing_labels, housing_predictions)
lin_rmse = np.sqrt(lin_mse)
lin_rmse
Out[70]: 68628.19819848922
from sklearn.metrics import mean_absolute_error
lin_mae = mean_absolute_error(housing_labels, housing_predictions)
lin_mae
Out[72]: 49439.89599001897
from sklearn.tree import DecisionTreeRegressor
tree_reg = DecisionTreeRegressor(random_state=42)
tree_reg.fit(housing_prepared, housing_labels)
Out[74]:
DecisionTreeRegressor(ccp_alpha=0.0, criterion='mse', max_depth=None,
max_features=None, max_leaf_nodes=None,
min_impurity_decrease=0.0, min_impurity_split=None,
min_samples_leaf=1, min_samples_split=2,
min_weight_fraction_leaf=0.0, presort='deprecated',
random_state=42, splitter='best')
housing_predictions = tree_reg.predict(housing_prepared)
tree_mse = mean_squared_error(housing_labels, housing_predictions)
tree_rmse = np.sqrt(tree_mse)
tree_rmse
Out[75]: 0.0
from sklearn.model_selection import cross_val_score
scores = cross_val_score(tree_reg, housing_prepared, housing_labels,
scoring="neg_mean_squared_error", cv=10)
tree_rmse_scores = np.sqrt(-scores)
def display_scores(scores):
print("Scores:", scores)
print("Mean:", scores.mean())
print("Standard deviation:", scores.std())
display_scores(tree_rmse_scores)
Scores: [70194.33680785 66855.16363941 72432.58244769 70758.73896782
71115.88230639 75585.14172901 70262.86139133 70273.6325285
75366.87952553 71231.65726027]
Mean: 71407.68766037929
Standard deviation: 2439.4345041191004
lin_scores = cross_val_score(lin_reg, housing_prepared, housing_labels,
scoring="neg_mean_squared_error", cv=10)
lin_rmse_scores = np.sqrt(-lin_scores)
display_scores(lin_rmse_scores)
Scores: [66782.73843989 66960.118071 70347.95244419 74739.57052552
68031.13388938 71193.84183426 64969.63056405 68281.61137997
71552.91566558 67665.10082067]
Mean: 69052.46136345083
Standard deviation: 2731.67400179835
from sklearn.ensemble import RandomForestRegressor
forest_reg = RandomForestRegressor(n_estimators=10, random_state=42)
forest_reg.fit(housing_prepared, housing_labels)
Out[82]:
RandomForestRegressor(bootstrap=True, ccp_alpha=0.0, criterion='mse',
max_depth=None, max_features='auto', max_leaf_nodes=None,
max_samples=None, min_impurity_decrease=0.0,
min_impurity_split=None, min_samples_leaf=1,
min_samples_split=2, min_weight_fraction_leaf=0.0,
n_estimators=10, n_jobs=None, oob_score=False,
random_state=42, verbose=0, warm_start=False)
housing_predictions = forest_reg.predict(housing_prepared)
forest_mse = mean_squared_error(housing_labels, housing_predictions)
forest_rmse = np.sqrt(forest_mse)
forest_rmse
Out[83]: 21933.31414779769
from sklearn.model_selection import cross_val_score
forest_scores = cross_val_score(forest_reg, housing_prepared, housing_labels,
scoring="neg_mean_squared_error", cv=10)
forest_rmse_scores = np.sqrt(-forest_scores)
display_scores(forest_rmse_scores)
Scores: [51646.44545909 48940.60114882 53050.86323649 54408.98730149
50922.14870785 56482.50703987 51864.52025526 49760.85037653
55434.21627933 53326.10093303]
Mean: 52583.72407377466
Standard deviation: 2298.353351147122
cores = cross_val_score(lin_reg, housing_prepared, housing_labels, scoring="neg_mean_squared_error", cv=10)
pd.Series(np.sqrt(-scores)).describe()
Out[86]:
count 10.000000
mean 71407.687660
std 2571.389745
min 66855.163639
25% 70265.554176
50% 70937.310637
75% 72132.351151
max 75585.141729
dtype: float64
from sklearn.svm import SVR
svm_reg = SVR(kernel="linear")
svm_reg.fit(housing_prepared, housing_labels)
housing_predictions = svm_reg.predict(housing_prepared)
svm_mse = mean_squared_error(housing_labels, housing_predictions)
svm_rmse = np.sqrt(svm_mse)
svm_rmse
Out[88]: 111094.6308539982
from sklearn.model_selection import GridSearchCV
param_grid = [
# try 12 (3×4) combinations of hyperparameters
{'n_estimators': [3, 10, 30], 'max_features': [2, 4, 6, 8]},
# then try 6 (2×3) combinations with bootstrap set as False
{'bootstrap': [False], 'n_estimators': [3, 10], 'max_features': [2, 3, 4]},
]
forest_reg = RandomForestRegressor(random_state=42)
grid_search = GridSearchCV(forest_reg, param_grid, cv=5,
scoring='neg_mean_squared_error', return_train_score=True)
grid_search.fit(housing_prepared, housing_labels)
Out[92]:
GridSearchCV(cv=5, error_score=nan,
estimator=RandomForestRegressor(bootstrap=True, ccp_alpha=0.0,
criterion='mse', max_depth=None,
max_features='auto',
max_leaf_nodes=None,
max_samples=None,
min_impurity_decrease=0.0,
min_impurity_split=None,
min_samples_leaf=1,
min_samples_split=2,
min_weight_fraction_leaf=0.0,
n_estimators=100, n_jobs=None,
oob_score=False, random_state=42,
verbose=0, warm_start=False),
iid='deprecated', n_jobs=None,
param_grid=[{'max_features': [2, 4, 6, 8],
'n_estimators': [3, 10, 30]},
{'bootstrap': [False], 'max_features': [2, 3, 4],
'n_estimators': [3, 10]}],
pre_dispatch='2*n_jobs', refit=True, return_train_score=True,
scoring='neg_mean_squared_error', verbose=0)
grid_search.best_params_
Out[93]: {'max_features': 8, 'n_estimators': 30}
grid_search.best_estimator_
Out[94]:
RandomForestRegressor(bootstrap=True, ccp_alpha=0.0, criterion='mse',
max_depth=None, max_features=8, max_leaf_nodes=None,
max_samples=None, min_impurity_decrease=0.0,
min_impurity_split=None, min_samples_leaf=1,
min_samples_split=2, min_weight_fraction_leaf=0.0,
n_estimators=30, n_jobs=None, oob_score=False,
random_state=42, verbose=0, warm_start=False)
cvres = grid_search.cv_results_
for mean_score, params in zip(cvres["mean_test_score"], cvres["params"]):
print(np.sqrt(-mean_score), params)
63669.11631261028 {'max_features': 2, 'n_estimators': 3}
55627.099719926795 {'max_features': 2, 'n_estimators': 10}
53384.57275149205 {'max_features': 2, 'n_estimators': 30}
60965.950449450494 {'max_features': 4, 'n_estimators': 3}
52741.04704299915 {'max_features': 4, 'n_estimators': 10}
50377.40461678399 {'max_features': 4, 'n_estimators': 30}
58663.93866579625 {'max_features': 6, 'n_estimators': 3}
52006.19873526564 {'max_features': 6, 'n_estimators': 10}
50146.51167415009 {'max_features': 6, 'n_estimators': 30}
57869.25276169646 {'max_features': 8, 'n_estimators': 3}
51711.127883959234 {'max_features': 8, 'n_estimators': 10}
49682.273345071546 {'max_features': 8, 'n_estimators': 30}
62895.06951262424 {'bootstrap': False, 'max_features': 2, 'n_estimators': 3}
54658.176157539405 {'bootstrap': False, 'max_features': 2, 'n_estimators': 10}
59470.40652318466 {'bootstrap': False, 'max_features': 3, 'n_estimators': 3}
52724.9822587892 {'bootstrap': False, 'max_features': 3, 'n_estimators': 10}
57490.5691951261 {'bootstrap': False, 'max_features': 4, 'n_estimators': 3}
51009.495668875716 {'bootstrap': False, 'max_features': 4, 'n_estimators': 10}
pd.DataFrame(grid_search.cv_results_)
Out[96]:
mean_fit_time std_fit_time ... mean_train_score std_train_score
0 0.051722 0.000872 ... -1.105559e+09 2.220402e+07
1 0.164845 0.001738 ... -5.818785e+08 7.345821e+06
2 0.494041 0.002765 ... -4.394734e+08 2.966320e+06
3 0.081424 0.002869 ... -9.848396e+08 4.084607e+07
4 0.261867 0.001331 ... -5.163863e+08 1.542862e+07
5 0.780019 0.002526 ... -3.879289e+08 8.571233e+06
6 0.107034 0.002449 ... -9.023976e+08 2.591445e+07
7 0.369006 0.009938 ... -5.013349e+08 3.100456e+06
8 1.106260 0.035422 ... -3.841296e+08 3.617057e+06
9 0.136634 0.000802 ... -8.883545e+08 2.750227e+07
10 0.460226 0.007790 ... -4.923911e+08 1.459294e+07
11 1.377972 0.016125 ... -3.810330e+08 4.871017e+06
12 0.082217 0.002136 ... 0.000000e+00 0.000000e+00
13 0.276661 0.018320 ... -6.056027e-01 1.181156e+00
14 0.105622 0.002854 ... -1.214568e+01 2.429136e+01
15 0.345308 0.004804 ... -5.272080e+00 8.093117e+00
16 0.127376 0.001752 ... 0.000000e+00 0.000000e+00
17 0.423098 0.004561 ... -3.028238e-03 6.056477e-03
[18 rows x 23 columns]
from sklearn.model_selection import RandomizedSearchCV
from scipy.stats import randint
param_distribs = {
'n_estimators': randint(low=1, high=200),
'max_features': randint(low=1, high=8),
}
forest_reg = RandomForestRegressor(random_state=42)
rnd_search = RandomizedSearchCV(forest_reg, param_distributions=param_distribs,
n_iter=10, cv=5, scoring='neg_mean_squared_error', random_state=42)
rnd_search.fit(housing_prepared, housing_labels)
Out[97]:
RandomizedSearchCV(cv=5, error_score=nan,
estimator=RandomForestRegressor(bootstrap=True,
ccp_alpha=0.0,
criterion='mse',
max_depth=None,
max_features='auto',
max_leaf_nodes=None,
max_samples=None,
min_impurity_decrease=0.0,
min_impurity_split=None,
min_samples_leaf=1,
min_samples_split=2,
min_weight_fraction_leaf=0.0,
n_estimators=100,
n_jobs=None, oob_score=Fals...
iid='deprecated', n_iter=10, n_jobs=None,
param_distributions={'max_features': <scipy.stats._distn_infrastructure.rv_frozen object at 0x00000264CDAC7D48>,
'n_estimators': <scipy.stats._distn_infrastructure.rv_frozen object at 0x00000264CDAC7A88>},
pre_dispatch='2*n_jobs', random_state=42, refit=True,
return_train_score=False, scoring='neg_mean_squared_error',
verbose=0)
cvres = rnd_search.cv_results_
for mean_score, params in zip(cvres["mean_test_score"], cvres["params"]):
print(np.sqrt(-mean_score), params)
49150.70756927707 {'max_features': 7, 'n_estimators': 180}
51389.889203389284 {'max_features': 5, 'n_estimators': 15}
50796.155224308866 {'max_features': 3, 'n_estimators': 72}
50835.13360315349 {'max_features': 5, 'n_estimators': 21}
49280.9449827171 {'max_features': 7, 'n_estimators': 122}
50774.90662363929 {'max_features': 3, 'n_estimators': 75}
50682.78888164288 {'max_features': 3, 'n_estimators': 88}
49608.99608105296 {'max_features': 5, 'n_estimators': 100}
50473.61930350219 {'max_features': 3, 'n_estimators': 150}
64429.84143294435 {'max_features': 5, 'n_estimators': 2}
feature_importances = grid_search.best_estimator_.feature_importances_
feature_importances
Out[99]:
array([7.33442355e-02, 6.29090705e-02, 4.11437985e-02, 1.46726854e-02,
1.41064835e-02, 1.48742809e-02, 1.42575993e-02, 3.66158981e-01,
5.64191792e-02, 1.08792957e-01, 5.33510773e-02, 1.03114883e-02,
1.64780994e-01, 6.02803867e-05, 1.96041560e-03, 2.85647464e-03])
extra_attribs = ["rooms_per_hhold", "pop_per_hhold", "bedrooms_per_room"]
cat_encoder = full_pipeline.named_transformers_["cat"]
cat_one_hot_attribs = list(cat_encoder.categories_[0])
attributes = num_attribs + extra_attribs + cat_one_hot_attribs
sorted(zip(feature_importances, attributes), reverse=True)
Out[100]:
[(0.36615898061813423, 'median_income'),
(0.16478099356159054, 'INLAND'),
(0.10879295677551575, 'pop_per_hhold'),
(0.07334423551601243, 'longitude'),
(0.06290907048262032, 'latitude'),
(0.056419179181954014, 'rooms_per_hhold'),
(0.053351077347675815, 'bedrooms_per_room'),
(0.04114379847872964, 'housing_median_age'),
(0.014874280890402769, 'population'),
(0.014672685420543239, 'total_rooms'),
(0.014257599323407808, 'households'),
(0.014106483453584104, 'total_bedrooms'),
(0.010311488326303788, '<1H OCEAN'),
(0.0028564746373201584, 'NEAR OCEAN'),
(0.0019604155994780706, 'NEAR BAY'),
(6.0280386727366e-05, 'ISLAND')]
extra_attribs = ["rooms_per_hhold", "pop_per_hhold", "bedrooms_per_room"]
cat_encoder = full_pipeline.named_transformers_["cat"]
cat_one_hot_attribs = list(cat_encoder.categories_[0])
attributes = num_attribs + extra_attribs + cat_one_hot_attribs
sorted(zip(feature_importances, attributes), reverse=True)
Out[101]:
[(0.36615898061813423, 'median_income'),
(0.16478099356159054, 'INLAND'),
(0.10879295677551575, 'pop_per_hhold'),
(0.07334423551601243, 'longitude'),
(0.06290907048262032, 'latitude'),
(0.056419179181954014, 'rooms_per_hhold'),
(0.053351077347675815, 'bedrooms_per_room'),
(0.04114379847872964, 'housing_median_age'),
(0.014874280890402769, 'population'),
(0.014672685420543239, 'total_rooms'),
(0.014257599323407808, 'households'),
(0.014106483453584104, 'total_bedrooms'),
(0.010311488326303788, '<1H OCEAN'),
(0.0028564746373201584, 'NEAR OCEAN'),
(0.0019604155994780706, 'NEAR BAY'),
(6.0280386727366e-05, 'ISLAND')]
final_model = grid_search.best_estimator_
X_test = strat_test_set.drop("median_house_value", axis=1)
y_test = strat_test_set["median_house_value"].copy()
X_test_prepared = full_pipeline.transform(X_test)
final_predictions = final_model.predict(X_test_prepared)
final_mse = mean_squared_error(y_test, final_predictions)
final_rmse = np.sqrt(final_mse)
final_rmse
Out[103]: 47730.22690385927