Commit ⋅ shaypal5/skift

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@@ -95,7 +95,7 @@

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    def _input_col(self, X):
        pass  # pragma: no cover

    def fit(self, X, y):
    def fit(self, X, y, X_validation=None, y_validation=None):
        """Fits the classifier

        Parameters

@@ -104,6 +104,10 @@

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            The training input samples.
        y : array-like, shape = [n_samples]
            The target values. An array of int.
        X_validation : array-like, shape = [n_samples, n_features]
            The validation input samples.
        y_validation : array-like, shape = [n_samples]
            The validation target values. An array of int.

        Returns
        -------

@@ -114,10 +118,16 @@

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        self._validate_x(X)
        y = self._validate_y(y)
        input_col = self._input_col(X)
        if X_validation is not None:
            self._validate_x(X_validation)
            y_validation = self._validate_y(y_validation)
            input_col_validation = self._input_col(X_validation)
        else:
            input_col_validation = None

        return self._fit_input_col(input_col, y)
        return self._fit_input_col(input_col, y, input_col_validation, y_validation)

    def _fit_input_col(self, input_col, y):
    def _fit_input_col(self, input_col, y, input_col_validation=None, y_validation=None):
        # Store the classes seen during fit
        self.classes_ = unique_labels(y)
        self.num_classes_ = len(self.classes_)

@@ -126,9 +136,23 @@

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        # Dump training set to a fasttext-compatible file
        temp_trainset_fpath = temp_dataset_fpath()
        dump_xy_to_fasttext_format(input_col, y, temp_trainset_fpath)
        # train
        self.model = train_supervised(
            input=temp_trainset_fpath, **self.kwargs)
        if input_col_validation is not None:
            n_classes_validation = len(unique_labels(y_validation))
            assert n_classes_validation == self.num_classes_,\
                "Number of validation classes doesn't match number of training classes"
            temp_trainset_fpath_validation = temp_dataset_fpath()
            dump_xy_to_fasttext_format(input_col_validation, y_validation, temp_trainset_fpath_validation)
            # train
            self.model = train_supervised(
                input=temp_trainset_fpath, **{'autotuneValidationFile': temp_trainset_fpath_validation, **self.kwargs})
            try:
                os.remove(temp_trainset_fpath_validation)
            except FileNotFoundError:  # pragma: no cover
                pass
        else:
            self.model = train_supervised(
                input=temp_trainset_fpath, **self.kwargs)

        # Return the classifier
        try:
            os.remove(temp_trainset_fpath)

@@ -372,7 +396,7 @@

Loading

    def _input_col(self, X):
        pass

    def fit(self, X, y):
    def fit(self, X, y, X_validation=None, y_validation=None):
        """Fits the classifier

        Parameters

@@ -381,6 +405,10 @@

Loading

            The training input samples.
        y : array-like, shape = [n_samples]
            The target values. An array of int.
        X_validation : pd.Series
            The validation input samples.
        y_validation : array-like, shape = [n_samples]
            The validation target values. An array of int.

        Returns
        -------

@@ -393,7 +421,15 @@

Loading

        except AttributeError:
            input_col = X
        y = self._validate_y(y)
        return self._fit_input_col(input_col, y)
        if X_validation is not None:
            try:
                input_col_validation = X_validation.values
            except AttributeError:
                input_col_validation = X_validation
            y_validation = self._validate_y(y_validation)
        else:
            input_col_validation = None
        return self._fit_input_col(input_col, y, input_col_validation, y_validation)

    def _predict(self, X, k=1):
        # Ensure that fit had been called

Files		•	•	•	Coverage
skift	195	192	1	2	98.46%
Project Totals (2 files)	195	192	1	2	98.46%

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codecov:
  notify:
      require_ci_to_pass: yes

coverage:
  precision: 2
  round: down
  range: "70...100"
  ignore: Tests
  status:
    patch:
      default:
        target: '80'
    project: false

comment:
  layout: header, changes, diff
    behavior: default
    require_changes: false
    branches: null
    flags: null
    paths: null

ignore:
  - "versioneer.py"
  - "tests"
  - "**/_version.py"
  - "**/__init__.py"

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104	104		The training input samples.
105	105		y : array-like, shape = [n_samples]
106	106		The target values. An array of int.
	107	+	X_validation : array-like, shape = [n_samples, n_features]
	108	+	The validation input samples.
	109	+	y_validation : array-like, shape = [n_samples]
	110	+	The validation target values. An array of int.
107	111
108	112		Returns
109	113		-------

114	118		self._validate_x(X)
115	119		y = self._validate_y(y)
116	120		input_col = self._input_col(X)
	121	+	if X_validation is not None:
	122	+	self._validate_x(X_validation)
	123	+	y_validation = self._validate_y(y_validation)
	124	+	input_col_validation = self._input_col(X_validation)
	125	+	else:
	126	+	input_col_validation = None
117	127
118		-	return self._fit_input_col(input_col, y)
	128	+	return self._fit_input_col(input_col, y, input_col_validation, y_validation)
119	129
120		-	def _fit_input_col(self, input_col, y):
	130	+	def _fit_input_col(self, input_col, y, input_col_validation=None, y_validation=None):
121	131		# Store the classes seen during fit
122	132		self.classes_ = unique_labels(y)
123	133		self.num_classes_ = len(self.classes_)

126	136		# Dump training set to a fasttext-compatible file
127	137		temp_trainset_fpath = temp_dataset_fpath()
128	138		dump_xy_to_fasttext_format(input_col, y, temp_trainset_fpath)
129		-	# train
130		-	self.model = train_supervised(
131		-	input=temp_trainset_fpath, **self.kwargs)
	139	+	if input_col_validation is not None:
	140	+	n_classes_validation = len(unique_labels(y_validation))
	141	+	assert n_classes_validation == self.num_classes_,\
	142	+	"Number of validation classes doesn't match number of training classes"
	143	+	temp_trainset_fpath_validation = temp_dataset_fpath()
	144	+	dump_xy_to_fasttext_format(input_col_validation, y_validation, temp_trainset_fpath_validation)
	145	+	# train
	146	+	self.model = train_supervised(
	147	+	input=temp_trainset_fpath, {'autotuneValidationFile': temp_trainset_fpath_validation, self.kwargs})
	148	+	try:
	149	+	os.remove(temp_trainset_fpath_validation)
	150	+	except FileNotFoundError: # pragma: no cover
	151	+	pass
	152	+	else:
	153	+	self.model = train_supervised(
	154	+	input=temp_trainset_fpath, **self.kwargs)
	155	+
132	156		# Return the classifier
133	157		try:
134	158		os.remove(temp_trainset_fpath)

372	396		def _input_col(self, X):
373	397		pass
374	398
375		-	def fit(self, X, y):
	399	+	def fit(self, X, y, X_validation=None, y_validation=None):
376	400		"""Fits the classifier
377	401
378	402		Parameters

381	405		The training input samples.
382	406		y : array-like, shape = [n_samples]
383	407		The target values. An array of int.
	408	+	X_validation : pd.Series
	409	+	The validation input samples.
	410	+	y_validation : array-like, shape = [n_samples]
	411	+	The validation target values. An array of int.
384	412
385	413		Returns
386	414		-------

95	95		def _input_col(self, X):
96	96		pass # pragma: no cover
97	97
98		-	def fit(self, X, y):
	98	+	def fit(self, X, y, X_validation=None, y_validation=None):
99	99		"""Fits the classifier
100	100
101	101		Parameters

393	421		except AttributeError:
394	422		input_col = X
395	423		y = self._validate_y(y)
396		-	return self._fit_input_col(input_col, y)
	424	+	if X_validation is not None:
	425	+	try:
	426	+	input_col_validation = X_validation.values
	427	+	except AttributeError:
	428	+	input_col_validation = X_validation
	429	+	y_validation = self._validate_y(y_validation)
	430	+	else:
	431	+	input_col_validation = None
	432	+	return self._fit_input_col(input_col, y, input_col_validation, y_validation)
397	433
398	434		def _predict(self, X, k=1):
399	435		# Ensure that fit had been called

1	codecov:
2	notify:
3	require_ci_to_pass: yes
4
5	coverage:
6	precision: 2
7	round: down
8	range: "70...100"
9	ignore: Tests
10	status:
11	patch:
12	default:
13	target: '80'
14	project: false
15
16	comment:
17	layout: header, changes, diff
18	behavior: default
19	require_changes: false
20	branches: null
21	flags: null
22	paths: null
23
24	ignore:
25	- "versioneer.py"
26	- "tests"
27	- "**/_version.py"
28	- "**/__init__.py"

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