def variance_sweep(features, labels, seed, save_plot, show_plot, step=500):
"""
Performs a sweep across the range of variance to establish the value of removing
low variance pixels
"""
model = KMeans(n_clusters=10)
variance_analysis = []
selector = VarianceThreshold()
selector.fit(features)
# Sweeps through variance range
print("Performing sweep of variance thresholding...")
# The bounds correspond to approximately 0 features selected and all features selected
# lower bound 2900
# upper bound 6450
with click.progressbar(range(2900, 6450, step)) as variance_range:
for variance in variance_range:
selector.set_params(threshold=variance)
selected_features = selector.transform(features)
numpy.random.set_state(seed)
predictions = model.fit_predict(selected_features)
variance_analysis.append(
(variance, score_clustering(labels, predictions)))
# Plots results from variance sweep
if show_plot or save_plot:
data = list(zip(*[(x, *y.values()) for x, y in variance_analysis]))
name = "Variance"
handles = plt.plot(data[0], data[3], '-b', label=name + " V Score")
handles += plt.plot(data[0], data[4], '--b', label=name + " Rand")
plt.legend(handles, loc="lower left")
plt.xlabel("Variance Threshold for Feature Selection")
plt.title("Effect of Variance Threshold Feature Selection")
if save_plot is not None:
path = os.path.join(save_plot, "variance_sweep.png")
plt.savefig(path)
print("")
print("saved figure to " + path)
if show_plot:
plt.show()
plt.clf()
# Plots the variances of each pixel as a heatmap
plt.imshow(selector.variances_.reshape(48, -1),
cmap='hot',
interpolation='lanczos')
plt.title("Heatmap of Variances between Images")
if save_plot is not None:
path = os.path.join(save_plot, "variance_heatmap.png")
plt.savefig(path)
print("")
print("saved figure to " + path)
if show_plot:
plt.show()
plt.clf()
class VarianceThreshold(FeatureSelectionAlgorithm):
r"""Implementation of feature selection using variance threshold.
Date:
2020
Author:
Luka Pečnik
License:
MIT
Documentation:
https://scikit-learn.org/stable/modules/generated/sklearn.feature_selection.VarianceThreshold.html
See Also:
* :class:`niaaml.preprocessing.feature_selection.feature_selection_algorithm.FeatureSelectionAlgorithm`
"""
Name = 'Variance Threshold'
def __init__(self, **kwargs):
r"""Initialize VarianceThreshold feature selection algorithm.
"""
self._params = dict(
threshold=ParameterDefinition(MinMax(0, 0.1), np.float))
self.__variance_threshold = VarThr()
def set_parameters(self, **kwargs):
r"""Set the parameters/arguments of the algorithm.
"""
self.__variance_threshold.set_params(**kwargs)
def select_features(self, x, y, **kwargs):
r"""Perform the feature selection process.
Arguments:
x (pandas.core.frame.DataFrame): Array of original features.
y (pandas.core.series.Series) Expected classifier results.
Returns:
numpy.ndarray[bool]: Mask of selected features.
"""
self.__variance_threshold.fit(x)
return self.__variance_threshold.get_support()
def to_string(self):
r"""User friendly representation of the object.
Returns:
str: User friendly representation of the object.
"""
return FeatureSelectionAlgorithm.to_string(self).format(
name=self.Name,
args=self._parameters_to_string(
self.__variance_threshold.get_params()))
def main():
# get_ipython().run_line_magic('matplotlib', 'inline')
data_dir = 'data'
file_name = 'credit_card_default.csv'
#column_names = ID,LIMIT_BAL,SEX,EDUCATION,MARRIAGE,AGE,PAY_1,PAY_2,PAY_3,PAY_4,PAY_5,PAY_6,BILL_AMT1,BILL_AMT2,BILL_AMT3,BILL_AMT4,BILL_AMT5,BILL_AMT6,PAY_AMT1,PAY_AMT2,PAY_AMT3,PAY_AMT4,PAY_AMT5,PAY_AMT6,default payment next month
file_url = ''
if not is_running_from_ipython():
abspath = os.path.abspath('.')
file_url = 'file://' + abspath + os.path.sep + data_dir + os.path.sep + file_name
else:
file_url = os.path.join('../', data_dir, file_name)
# logging.debug('abspath %s', abspath)
pp = PredictiveProcessor()
pp.file_url = file_url
df = pp.data_read_csv()
pp.df = df
# pp.problem_understanding()
pp.data_preparation()
pp.data_analysis_exploratory()
pp.data_model_building()
pp.model_evaluation()
pp.model_deployment()
logger.info("LL: -----------------------------------------------")
# check is any null values
for column in df.columns:
# logger.info("LL: column %s datatype %s is having null : %s , about %s", column, df.dtypes[column], df[column].isnull().values.any(), df[column].isnull().values.sum())
# logger.info("LL: column %s datatype %s is having NA : %s , about %s", column, df.dtypes[column], df[column].isna().values.any(), df[column].isna().values.sum())
if (df.dtypes[column], df[column].isnull().values.any()
and df.dtypes[column], df[column].isna().values.any()):
if (df.dtypes[column] == 'int64'):
logger.info("LL: yes int64")
# df_example = df[column].fillna(0)
else:
logger.info("LL: not int64")
#alternatively use missingno
#-----------missingno
logger.info("LL: df.shape \n%s", df.shape)
logger.info("LL: df correlation \n%s", df.corr().round(2))
logger.info("LL: df correlation columns \n%s", df.corr().columns)
logger.info("LL: df correlation index \n%s", df.corr().index)
logger.info("LL: df covariance \n%s", df.cov().round(2))
logger.info("LL: df covariance columns \n%s", df.cov().columns)
logger.info("LL: df covariance index \n%s", df.cov().index)
# drop rows with null values
df_dropped_rows_na = df.dropna(axis=0)
logger.info("LL: df_dropped_rows_na.shape %s", df_dropped_rows_na.shape)
# drop columns with null values
df_dropped_cols_na = df.dropna(axis=1)
logger.info("LL: df_dropped_cols_na.shape %s", df_dropped_cols_na.shape)
#impute nan with new values
# missing_values = form of missing values in your data. (For example nan, 0, or "n/a".
# strategy = how to impute (choices are "mean", "median", "most frequent", and "constant".
# If you pass strategy=constant, then you can use the optional argument fill_value to pass your constant.
imputer = SimpleImputer(missing_values=np.nan, strategy='mean')
cols_to_impute = df.columns
out_imp = imputer.fit_transform(df[cols_to_impute])
df_new = pd.DataFrame(data=out_imp, columns=cols_to_impute)
# df_new = pd.concat([df_new, df[['species']]], axis = 1)
minmax_scaler = MinMaxScaler()
cols_to_minmaxscale = df.columns
out_scaled_minmax = minmax_scaler.fit_transform(df[cols_to_minmaxscale])
standard_scaler = StandardScaler()
cols_to_standardscale = df.columns
out_scaled_standard = standard_scaler.fit_transform(
df[cols_to_standardscale])
# encode categorical nonint features
categorical_features_nonint = []
for column in df.columns:
if (df.dtypes[column] != 'int64' or df.dtypes[column] != 'int32'):
logger.info("LL: no int64 or int32")
categorical_features_nonint.append(column)
# df_example = df[column].fillna(0)
else:
logger.info("LL: yes int64 or int32")
from sklearn.preprocessing import OrdinalEncoder
enc_ordinal = OrdinalEncoder()
out_enc_ord_catg_feat_nonint = enc_ordinal.fit_transform(
df[categorical_features_nonint])
logger.info("LL: out_enc categories \n%s", enc_ordinal.categories_)
logger.info("LL: out_enc \n%s", out_enc_ord_catg_feat_nonint)
df[categorical_features_nonint] = out_enc_ord_catg_feat_nonint
logger.info("LL: df_new \n%s", df.head())
# One-hot Enconding
from sklearn.preprocessing import OneHotEncoder
enc_onehot = OneHotEncoder(sparse=False)
out_enc_onehot_catg_feat_nonint = enc_onehot.fit_transform(
df[categorical_features_nonint])
new_cols_onehot_catg_feat_nonint = enc_onehot.get_feature_names(
categorical_features_nonint).tolist()
logger.info("LL: new_cols \n%s", new_cols_onehot_catg_feat_nonint)
# Label encoding
from sklearn import preprocessing
enc_label = preprocessing.LabelEncoder()
out_enc_label = enc_label.fit_transform(categorical_features_nonint)
# Dimension Reduction
# Feature Selection
# Feature Filtering
# Variance Treshold
# Correlation Coefficient
#Variance Treshold
from sklearn.feature_selection import VarianceThreshold
selector = VarianceThreshold()
cols = df.columns
# cols = categorical_features_nonint
selector.fit(df[cols])
# check feature variances before selection
logger.info("LL: variance treshold \n%s", selector.variances_)
# set threshold into selector object
selector.set_params(threshold=1.0)
out_sel = selector.fit_transform(df[cols])
logger.info("LL: selector.get_support() \n%s", selector.get_support)
df_sel = df.iloc[:, selector.get_support()]
# add labels to new dataframe and sanity check
df_sel = pd.concat([df_sel, df[['default payment next month']]], axis=1)
logger.info("LL: df_sel.head() \n%s", df_sel.head())
#Correlation Coefficient
cor = df.corr()
sns.heatmap(cor, annot=False, cmap=plt.cm.Blues)
logger.info("LL: plt.show() \n%s", plt.show())
# get correlation values with target variable
cor_target = abs(cor['default payment next month'])
logger.info("LL: cor_target \n%s", cor_target)
#For demonstration purposes, we will choose 0.6 as the threshold and then filter. From the output, you should expect columns 5 and 12 (0.69 and 0.74) to be selected:
selected_cols = cor_target[cor_target > 0.6]
logger.info("LL: selected columns, correlation with target > 0.6")
logger.info("LL: selected_cols \n%s", selected_cols)
# filter in the selected features
df_sel = df[selected_cols.index]
logger.info("LL: df_sel.head() \n%s", def_sel.head())
# Wrapper Methods
# Sequential Feature Selection
# Forward Sequential Selection and Backward Sequential Selection
# LinearRegression() for continuous target variables and RandomForestClassifier() for categorical target variables
# We will use the Support Vector Machine Classifier ("SVC") as the estimator for our example RFE.
# Now let's import our modules and define the independent (X) and dependent (y) variables for the SVC:
from sklearn.feature_selection import RFE
from sklearn.svm import SVC
cols = df.columns
X = df[cols]
y = df['default payment next month']
svc = SVC(kernel="linear", C=1)
rfe = RFE(estimator=svc, n_features_to_select=2, step=1)
rfe.fit(X, y)
logger.info("LL: cols \n%s", cols)
logger.info("LL: rfe.ranking_")
logger.info("LL: -----------------------------------------------")
