def plot_cat_scatter(df, x_cols, y, format='png'):
if not isinstance(df, pd.DataFrame):
raise DataFrameTypeError('df', df)
if y in x_cols:
from util.exceptions import ConfigError
raise ConfigError(
'The label "{}" was also configured as a feature in x_cols for function "plot_cat_scatter".'
.format(y))
import seaborn as sns
import matplotlib.ticker as ticker
# TODO: check y is not in x_cols
for col in x_cols:
g = sns.catplot(x=col, y=y, data=df)
ax = g.axes[0, 0]
ax.set_xticklabels(df[col].unique().tolist(), rotation=90)
N = df[col].nunique()
plt.xticks(range(int(N)))
plt.gca().margins(x=0)
plt.gcf().canvas.draw()
m = 0.1
margin = m / plt.gcf().get_size_inches()[0]
plt.gcf().subplots_adjust(left=margin, right=1. - margin, bottom=0.333)
plt.gcf().set_size_inches(30, 7)
plt.savefig('./tmp/images/{}_to_{}_cat_scatter.{}'.format(
to_file_save_name(col), to_file_save_name(y), format),
format=format)
plt.close(plt.gcf())
def train_val_test_split(df, label=None, split=[0.6, 0.2, 0.2], seed=None):
if not isinstance(df, pd.DataFrame):
raise DataFrameTypeError('df', df)
if not np.sum(split) == 1.0:
raise DataSplitError(np.sum(split))
df_new = df.copy()
if seed == None or seed == 'None':
df_new = shuffle(df_new)
else:
df_new = shuffle(df_new, random_state=seed)
m = len(df_new)
train_end = int(split[0] * m)
test_end = int(split[1] * m) + train_end
train = df_new[:train_end]
test = df_new[train_end:test_end]
val = df_new[test_end:]
if label == None:
return train.values, _, test.values, _, val.values, _
y_train = train[label].values
X_train = train.drop([label], axis=1).values
y_test = test[label].values
X_test = test.drop([label], axis=1).values
y_val = val[label].values
X_val = val.drop([label], axis=1).values
return X_train, y_train, X_test, y_test, X_val, y_val
def apply_binary_encoding(df, categorical_columns):
if not isinstance(df, pd.DataFrame):
raise DataFrameTypeError('df', df)
import category_encoders as ce
encoder = ce.BinaryEncoder(cols=categorical_columns).fit(df.values)
X_transformed = encoder.transform(df)
return X_transformed
def describe_data(df, name, n, include='all'):
if not isinstance(df, pd.DataFrame):
raise DataFrameTypeError('df', df)
file_content = 'Data description for {}\n'.format(name)
file_content += df.describe(include=include).T.to_string()
file_content += '\n\nThe data set has {} columns.'.format(len(df.columns))
file_content += '\n\nThe data set has {} NaN values.'.format(
df.isnull().values.sum())
file_content += '\n\nDataFrame data types:\n\n'
file_content += df.dtypes.to_string()
file_content += '\n\nDataFrame head{}:\n\n'.format(n)
file_content += df.head(n).to_string()
file_content += '\n\n DataFrame tail{}:\n\n'.format(n)
file_content += df.tail(n).to_string()
to_txt_with_versioning('./tmp/description/{}'.format(name), file_content)
def apply_weight_of_evidence_encoding(df, categorical_columns, label='y'):
if not isinstance(df, pd.DataFrame):
raise DataFrameTypeError('df', df)
import category_encoders as ce
encoder = ce.WOEEncoder(cols=categorical_columns).fit(
df.drop([label], axis=1), df[label])
X_transformed = encoder.transform(df)
return X_transformed
def drop_csv_column(df):
if not isinstance(df, pd.DataFrame):
raise DataFrameTypeError('df', df)
if 'Unnamed: 0' in df.columns:
df.drop(['Unnamed: 0'], axis='columns', inplace=True)
if 'Unnamed: 0.1' in df.columns:
df.drop(['Unnamed: 0.1'], axis='columns', inplace=True)
return df
def apply_sum_encoding(df, categorical_columns):
if not isinstance(df, pd.DataFrame):
raise DataFrameTypeError('df', df)
import category_encoders as ce
encoder = ce.SumEncoder(cols=categorical_columns).fit(df.values)
X_transformed = encoder.transform(df)
X.drop(['intercept'], inplace=True, axis=1)
return X_transformed
def extract_X_y(df, label):
if not isinstance(df, pd.DataFrame):
raise DataFrameTypeError('df', df)
if label == None:
y = None
else:
y = df[label].values
X = df.drop([label], axis=1).values
return X, y
def clean_labels(df, label):
if not isinstance(df, pd.DataFrame):
raise DataFrameTypeError('df', df)
classes = df[label].unique().tolist()
def transform_labels(row, label, classes):
return classes.index(row[label])
df[label] = df.apply(transform_labels, args=(label, classes), axis=1)
return df
def apply_leave_one_out_encoding(df, categorical_columns, label='y'):
if not isinstance(df, pd.DataFrame):
raise DataFrameTypeError('df', df)
import category_encoders as ce
encoder = ce.LeaveOneOutEncoder(return_df=True,
cols=categorical_columns).fit(
df.drop([label], axis=1), df[label])
X_transformed = encoder.transform(df.drop([label], axis=1))
X_transformed[label] = df[label]
return X_transformed
def plot_regression(df, cols, cat_cols, format='png'):
if not isinstance(df, pd.DataFrame):
raise DataFrameTypeError('df', df)
import seaborn as sns
import itertools
for x_col, y_col in itertools.combinations(cols, 2):
for cat_col in cat_cols:
g = sns.lmplot(x=x_col, y=y_col, col=cat_col, data=df)
plt.savefig('./tmp/images/{}_to_{}_by_{}_regression.{}'.format(
to_file_save_name(x_col), to_file_save_name(y_col),
to_file_save_name(cat_col), format),
format=format)
plt.close(plt.gcf())
def clean_numeric(df, cols):
if not isinstance(df, pd.DataFrame):
raise DataFrameTypeError('df', df)
def is_number(s):
try:
float(s)
return True
except ValueError:
return False
for col in cols:
df = df[df[col].apply(lambda x: is_number(x))]
return df
def apply_pca(df, label=None, n_components=None):
if not isinstance(df, pd.DataFrame):
raise DataFrameTypeError('df', df)
from sklearn.decomposition import PCA
pca = PCA(n_components=n_components)
if label == None:
X = pd.DataFrame(pca.fit_transform(df),
columns=['PCA%i' % i for i in range(n_components)],
index=df.index)
else:
X = pd.DataFrame(pca.fit_transform(df.drop([label], axis=1)),
columns=['PCA%i' % i for i in range(n_components)],
index=df.index)
X[label] = df[label]
return X
def plot_scatter(df, cols, y, format='png'):
if not isinstance(df, pd.DataFrame):
raise DataFrameTypeError('df', df)
if y in cols:
from util.exceptions import ConfigError
raise ConfigError(
'The label "{}" was also configured as a feature in cols for function "plot_scatter".'
.format(y))
import seaborn as sns
import itertools
for x_col, y_col in itertools.combinations(cols, 2):
g = sns.scatterplot(x=x_col, y=y_col, hue=y, data=df, s=10, alpha=0.4)
plt.savefig('./tmp/images/{}_to_{}_by_{}_scatter.{}'.format(
to_file_save_name(x_col), to_file_save_name(y_col),
to_file_save_name(y), format),
format=format)
plt.close(plt.gcf())
def plot_boxplots(df, columns=-1, label=None, format='png'):
if not isinstance(df, pd.DataFrame):
raise DataFrameTypeError('df', df)
if columns == -1:
columns = df.columns
for col in columns:
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
if label == None or col == label:
df.boxplot(column=col, ax=ax)
else:
df.boxplot(column=col, by=label, ax=ax)
plt.savefig('./tmp/images/{}_box.{}'.format(to_file_save_name(col),
format),
format=format)
plt.close(fig)
def plot_cat_point(df, x_cols, y, format='png'):
if not isinstance(df, pd.DataFrame):
raise DataFrameTypeError('df', df)
if y in x_cols:
from util.exceptions import ConfigError
raise ConfigError(
'The label "{}" was also configured as a feature in x_cols for function "plot_cat_point".'
.format(y))
import seaborn as sns
import matplotlib.ticker as ticker
import itertools
for x_col, hue_col in itertools.combinations(x_cols, 2):
g = sns.catplot(x=x_col, y=y, hue=hue_col, kind='point', data=df)
ax = g.axes[0, 0]
ax.set_xticklabels(df[x_col].unique().tolist(), rotation=90)
N = df[x_col].nunique()
plt.xticks(range(int(N)))
plt.gca().margins(x=0)
plt.gcf().canvas.draw()
m = 0.1
margin = m / plt.gcf().get_size_inches()[0]
plt.gcf().subplots_adjust(left=margin, right=1. - margin, bottom=0.333)
plt.gcf().set_size_inches(30, 7)
# ax = g.axes[0,0]
# N = df[col].nunique()
# plt.xticks(range(N))
# plt.gca().margins(x=0)
# plt.gcf().canvas.draw()
# tl = plt.gca().get_xticklabels()
# maxsize = max([t.get_window_extent().width for t in tl])
# m = 0.2 # inch margin
# s = maxsize/plt.gcf().dpi*N+2*m
# margin = m/plt.gcf().get_size_inches()[0]
#
# plt.gcf().subplots_adjust(left=margin, right=1.-margin)
# plt.gcf().set_size_inches(s, plt.gcf().get_size_inches()[1])
plt.savefig('./tmp/images/{}_to_{}_hue_{}_cat_point.{}'.format(
to_file_save_name(x_col), to_file_save_name(y),
to_file_save_name(hue_col), format),
format=format)
plt.close(plt.gcf())
def replace_NaN_with_mean(df):
if not isinstance(df, pd.DataFrame):
raise DataFrameTypeError('df', df)
mean = df.mean(numeric_only=True).to_dict()
df.fillna(mean, inplace=True)
return df
def remove_NaN_columns(df, how='any'):
if not isinstance(df, pd.DataFrame):
raise DataFrameTypeError('df', df)
df.dropna(how=how, axis='columns', inplace=True)
return df
def apply_pca(df, categorical_columns, label='y'):
if not isinstance(df, pd.DataFrame):
raise DataFrameTypeError('df', df)
# TODO: implement
return None
def apply_dummy_encoding(df):
if not isinstance(df, pd.DataFrame):
raise DataFrameTypeError('df', df)
X_transformed = pd.get_dummies(df)
return X_transformed
def drop_columns(df, cols):
if not isinstance(df, pd.DataFrame):
raise DataFrameTypeError('df', df)
df.drop(cols, axis='columns', inplace=True)
return df
def apply_standard_scaler_scaling(df):
if not isinstance(df, pd.DataFrame):
raise DataFrameTypeError('df', df)
from sklearn.preprocessing import StandardScaler
df[df.columns] = StandardScaler().fit_transform(df[df.columns])
return df
