import pandas as pd
from evidently import Report
from evidently.metrics import *
from evidently.presets import *
from evidently import Dataset, DataDefinition
from evidently import BinaryClassification, MulticlassClassification
y_train = pd.Series([1, 0, 0, 1, 1, 0, 1, 0, 0, 0])
y_train = y_train.to_frame(name = 'species')
y_pred = pd.Series([0.46, 0.2, 0.1, 0.6, 0.7, 0.7, 0.55, 0.8, 0.3, 0.2])
y_pred = y_pred.to_frame(name = 'prediction')
y_eval = pd.concat([y_train, y_pred], axis=1)
num_class = y_train[colname_train].nunique()
data_definition = DataDefinition(
classification=[BinaryClassification(
target = colname_train,
prediction_labels = colname_pred,
categorical_columns=[colname_train, colname_pred])])
y_eval_data = Dataset.from_pandas(y_eval, data_definition = data_definition)
report = Report(metrics=[ClassificationPreset()],
include_tests=True,
report_options=[
("evidently.options.ColorOptions", {
"primary_color": "#5a86ad",
"heatmap": 'Viridis'
})
]
)
import pandas as pd
from evidently import Report
from evidently.metrics import *
from evidently.presets import *
from evidently import Dataset, DataDefinition
from evidently import BinaryClassification, MulticlassClassification
y_train = pd.Series([1, 0, 0, 1, 1, 0, 1, 0, 0, 0])
y_train = y_train.to_frame(name = 'species')
y_pred = pd.Series([0.46, 0.2, 0.1, 0.6, 0.7, 0.7, 0.55, 0.8, 0.3, 0.2])
y_pred = y_pred.to_frame(name = 'prediction')
y_eval = pd.concat([y_train, y_pred], axis=1)
num_class = y_train[colname_train].nunique()
data_definition = DataDefinition(
classification=[BinaryClassification(
target = colname_train,
prediction_labels = colname_pred,
categorical_columns=[colname_train, colname_pred])])
y_eval_data = Dataset.from_pandas(y_eval, data_definition = data_definition)
report = Report(metrics=[ClassificationPreset()],
include_tests=True,
report_options=[
("evidently.options.ColorOptions", {
"primary_color": "#5a86ad",
"heatmap": 'Viridis'
})
]
)