def distance(instance_a, instance_b, **params):
# find distance
instance_a = from_nested_to_2d_array(
instance_a, return_numpy=True) # todo use specific
# dimension rather than whole
# thing?
instance_b = from_nested_to_2d_array(
instance_b, return_numpy=True) # todo use specific
# dimension rather than whole thing?
instance_a = np.transpose(instance_a)
instance_b = np.transpose(instance_b)
return distance_measure(instance_a, instance_b, **params)
def test_from_nested_to_2d_array(n_instances, n_columns, n_timepoints):
"""Test from_nested_to_2d_array for correctness."""
nested, _ = make_classification_problem(n_instances, n_columns, n_timepoints)
array = from_nested_to_2d_array(nested)
assert array.shape == (n_instances, n_columns * n_timepoints)
assert array.index.equals(nested.index)
def transform(self, X, y=None):
"""Concatenate multivariate time series/panel data into long
univariate time series/panel
data by simply concatenating times series in time.
Parameters
----------
X : nested pandas DataFrame of shape [n_samples, n_features]
Nested dataframe with time-series in cells.
Returns
-------
Xt : pandas DataFrame
Transformed pandas DataFrame with same number of rows and single
column
"""
self.check_is_fitted()
X = check_X(X)
# We concatenate by tabularizing all columns and then detabularizing
# them into a single column
if isinstance(X, pd.DataFrame):
Xt = from_nested_to_2d_array(X)
else:
Xt = from_3d_numpy_to_2d_array(X)
return from_2d_array_to_nested(Xt)
def test_output_format_dim(len_series, n_instances, n_components):
np.random.seed(42)
X = from_2d_array_to_nested(
pd.DataFrame(data=np.random.randn(n_instances, len_series)))
trans = PCATransformer(n_components=n_components)
Xt = trans.fit_transform(X)
# Check number of rows and output type.
assert isinstance(Xt, pd.DataFrame)
assert Xt.shape[0] == X.shape[0]
# Check number of principal components in the output.
assert from_nested_to_2d_array(Xt).shape[1] == min(
n_components,
from_nested_to_2d_array(X).shape[1])
def _combine_data_frames(self, dataFrames, weighting_factor, col_names):
"""Combine two dataframes together into a single dataframe.
Used when the shape_descriptor_function is set to "compound".
"""
first_desc = dataFrames[0]
second_desc = dataFrames[1]
first_desc_array = []
second_desc_array = []
# Convert the dataframes into arrays
for x in first_desc.columns:
first_desc_array.append(
from_nested_to_2d_array(first_desc[x], return_numpy=True))
for x in second_desc.columns:
second_desc_array.append(
from_nested_to_2d_array(second_desc[x], return_numpy=True))
# Concatenate the arrays together
res = []
for x in range(len(first_desc_array)):
dim1 = []
for y in range(len(first_desc_array[x])):
dim2 = []
dim2.extend(first_desc_array[x][y])
dim2.extend(second_desc_array[x][y] * weighting_factor)
dim1.append(dim2)
res.append(dim1)
res = np.asarray(res)
# Convert to pandas dataframe
df = pd.DataFrame()
for col in col_names:
colToAdd = []
for row in range(len(res[col])):
inst = res[col][row]
colToAdd.append(pd.Series(inst))
df[col] = colToAdd
return df
def row_first(X):
if isinstance(X, pd.Series):
X = pd.DataFrame(X)
Xt = pd.concat(
[
pd.Series(from_nested_to_2d_array(col).iloc[:, 0])
for _, col in X.items()
],
axis=1,
)
return Xt
def test_padding_fill_value_transformer():
"""Test full fill padding."""
# load data
X_train, y_train = load_basic_motions(split="train", return_X_y=True)
padding_transformer = PaddingTransformer(pad_length=120, fill_value=1)
Xt = padding_transformer.fit_transform(X_train)
# when we tabularize the data it has 6 dimensions
# and we've padded them all to 120 long.
data = from_nested_to_2d_array(Xt)
assert len(data.columns) == 120 * 6
def test_padding_transformer():
"""Test the dimensions after padding."""
# load data
X_train, y_train = load_basic_motions(split="train", return_X_y=True)
padding_transformer = PaddingTransformer()
Xt = padding_transformer.fit_transform(X_train)
# when we tabularize the data it has 6 dimensions
# and we've padded them to there normal length of 100
data = from_nested_to_2d_array(Xt)
assert len(data.columns) == 100 * 6
def test_truncation_transformer():
"""Test truncation to the shortest series length."""
# load data
X_train, y_train = load_basic_motions(split="train", return_X_y=True)
truncated_transformer = TruncationTransformer(5)
Xt = truncated_transformer.fit_transform(X_train)
# when we tabularize the data it has 6 dimensions
# and we've truncated them all to 5 long.
data = from_nested_to_2d_array(Xt)
assert len(data.columns) == 5 * 6
def test_truncation_paramterised_transformer():
"""Test truncation to the a user defined length."""
# load data
X_train, y_train = load_basic_motions(split="train", return_X_y=True)
truncated_transformer = TruncationTransformer(2, 10)
Xt = truncated_transformer.fit_transform(X_train)
# when we tabularize the data it has 6 dimensions
# and we've truncated them all to (10-2) long.
data = from_nested_to_2d_array(Xt)
assert len(data.columns) == 8 * 6
def test_dft_mft(use_fallback_dft, norm):
# load training data
X, y = load_gunpoint(split="train", return_X_y=True)
X_tab = from_nested_to_2d_array(X, return_numpy=True)
word_length = 6
alphabet_size = 4
# Single DFT transformation
window_size = np.shape(X_tab)[1]
p = SFA(
word_length=6,
alphabet_size=4,
window_size=window_size,
norm=norm,
use_fallback_dft=use_fallback_dft,
).fit(X, y)
if use_fallback_dft:
dft = p._discrete_fourier_transform(X_tab[0], word_length, norm, 1, True)
else:
dft = p._fast_fourier_transform(X_tab[0])
mft = p._mft(X_tab[0])
assert (mft - dft < 0.0001).all()
# Windowed DFT transformation
window_size = 140
p = SFA(
word_length=word_length,
alphabet_size=alphabet_size,
window_size=window_size,
norm=norm,
use_fallback_dft=use_fallback_dft,
).fit(X, y)
mft = p._mft(X_tab[0])
for i in range(len(X_tab[0]) - window_size + 1):
if use_fallback_dft:
dft = p._discrete_fourier_transform(
X_tab[0, i : window_size + i], word_length, norm, 1, True
)
else:
dft = p._fast_fourier_transform(X_tab[0, i : window_size + i])
assert (mft[i] - dft < 0.001).all()
assert len(mft) == len(X_tab[0]) - window_size + 1
assert len(mft[0]) == word_length
def test_tsfresh_extractor(default_fc_parameters):
X, y = make_classification_problem()
X_train, X_test, y_train, y_test = train_test_split(X, y)
transformer = TSFreshFeatureExtractor(
default_fc_parameters=default_fc_parameters, disable_progressbar=True
)
Xt = transformer.fit_transform(X_train, y_train)
actual = Xt.filter(like="__mean", axis=1).values.ravel()
expected = from_nested_to_2d_array(X_train).mean(axis=1).values
assert expected[0] == X_train.iloc[0, 0].mean()
np.testing.assert_allclose(actual, expected)
def test_pca_results(n_components):
np.random.seed(42)
# sklearn
X = pd.DataFrame(data=np.random.randn(10, 5))
pca = PCA(n_components=n_components)
Xt1 = pca.fit_transform(X)
# sktime
Xs = from_2d_array_to_nested(X)
pca_transform = PCATransformer(n_components=n_components)
Xt2 = pca_transform.fit_transform(Xs)
assert np.allclose(np.asarray(Xt1),
np.asarray(from_nested_to_2d_array(Xt2)))
def transform(self, X, y=None):
"""
Parameters
----------
X : a pandas dataframe of shape = [n_samples, num_dims]
The training input samples.
Returns
-------
df: a pandas data frame of shape = [num_intervals, num_dims]
"""
# Check the data
self.check_is_fitted()
X = check_X(X, coerce_to_pandas=True)
# Get information about the dataframe
n_timepoints = len(X.iloc[0, 0])
num_instances = X.shape[0]
col_names = X.columns
self._check_parameters(n_timepoints)
df = pd.DataFrame()
for x in col_names:
# Convert one of the columns in the dataframe to numpy array
arr = from_nested_to_2d_array(pd.DataFrame(X[x]), return_numpy=True)
# Calculate gradients
transformedData = []
for y in range(num_instances):
res = self._get_gradients_of_lines(arr[y])
transformedData.append(res)
# Convert to Numpy array
transformedData = np.asarray(transformedData)
# Add it to the dataframe
colToAdd = []
for i in range(len(transformedData)):
inst = transformedData[i]
colToAdd.append(pd.Series(inst))
df[x] = colToAdd
return df
def plot_cluster_algorithm(model: BaseClusterer, predict_series: NumpyOrDF,
k: int):
"""
Method that is used to plot a clustering algorithms output
Parameters
----------
model: BaseClusterer
Clustering model to plot
predict_series: Numpy or Dataframe
The series to predict the values for
k: int
Number of centers
"""
_check_soft_dependencies("matplotlib")
import matplotlib.pyplot as plt
import matplotlib.patches as mpatches
if isinstance(predict_series, pd.DataFrame):
predict_series = from_nested_to_2d_array(predict_series,
return_numpy=True)
plt.figure(figsize=(5, 10))
plt.rcParams["figure.dpi"] = 100
indexes = model.predict(predict_series)
centers = model.get_centers()
series_values = TimeSeriesLloydsPartitioning.get_cluster_values(
indexes, predict_series, k)
fig, axes = plt.subplots(nrows=k, ncols=1)
for i in range(k):
_plot(series_values[i], centers[i], axes[i])
blue_patch = mpatches.Patch(color="blue",
label="Series that belong to the cluster")
red_patch = mpatches.Patch(color="red", label="Cluster centers")
plt.legend(
handles=[red_patch, blue_patch],
loc="upper center",
bbox_to_anchor=(0.5, -0.40),
fancybox=True,
shadow=True,
ncol=5,
)
plt.tight_layout()
plt.show()
def _perform_paa_along_dim(self, X):
X = from_nested_to_2d_array(X, return_numpy=True)
num_atts = X.shape[1]
num_insts = X.shape[0]
dims = pd.DataFrame()
data = []
for i in range(num_insts):
series = X[i, :]
frames = []
current_frame = 0
current_frame_size = 0
frame_length = num_atts / self.num_intervals
frame_sum = 0
for n in range(num_atts):
remaining = frame_length - current_frame_size
if remaining > 1:
frame_sum += series[n]
current_frame_size += 1
else:
frame_sum += remaining * series[n]
current_frame_size += remaining
if current_frame_size == frame_length:
frames.append(frame_sum / frame_length)
current_frame += 1
frame_sum = (1 - remaining) * series[n]
current_frame_size = 1 - remaining
# if the last frame was lost due to double imprecision
if current_frame == self.num_intervals - 1:
frames.append(frame_sum / frame_length)
data.append(pd.Series(frames))
dims[0] = data
return dims
def transform(self, X, y=None):
"""
Parameters
----------
X : a pandas dataframe of shape = [n_samples, num_dims]
The training input samples.
Returns
-------
dims: a pandas data frame of shape
= [n_samples, num_dims]
"""
# Check the data
self.check_is_fitted()
X = check_X(X, enforce_univariate=False, coerce_to_pandas=True)
self._check_parameters()
# Get information about the dataframe
col_names = X.columns
df = pd.DataFrame()
for x in col_names:
# Convert one of the columns in the dataframe to numpy array
arr = from_nested_to_2d_array(pd.DataFrame(X[x]),
return_numpy=True)
transformedData = self._extract_wavelet_coefficients(arr)
# Convert to a numpy array
transformedData = np.asarray(transformedData)
# Add it to the dataframe
colToAdd = []
for i in range(len(transformedData)):
inst = transformedData[i]
colToAdd.append(pd.Series(inst))
df[x] = colToAdd
return df
def transform(self, X, y=None):
"""Transform nested pandas dataframe into tabular dataframe.
Parameters
----------
X : pandas DataFrame
Nested dataframe with pandas series or numpy arrays in cells.
y : array-like, optional (default=None)
Returns
-------
Xt : pandas DataFrame
Transformed dataframe with only primitives in cells.
"""
self.check_is_fitted()
X = check_X(X)
if isinstance(X, pd.DataFrame):
return from_nested_to_2d_array(X)
else:
return from_3d_numpy_to_2d_array(X)
def _transform_single_feature(self, X, feature):
"""transforms data into a specified catch22 feature
Parameters
----------
X : pandas DataFrame, input time series
feature : int, catch22 feature id or String, catch22 feature
name.
Returns
-------
Numpy array containing a catch22 feature for each input series
"""
if isinstance(feature,
(int, np.integer)) or isinstance(feature,
(float, np.float)):
if feature > 21 or feature < 0:
raise ValueError("Invalid catch22 feature ID")
elif isinstance(feature, str):
if feature in feature_names:
feature = feature_names.index(feature)
else:
raise ValueError("Invalid catch22 feature name")
else:
raise ValueError("catch22 feature name or ID required")
if isinstance(X, pd.DataFrame):
X = from_nested_to_2d_array(X, return_numpy=True)
n_instances = X.shape[0]
X = np.reshape(X, (n_instances, -1))
c22_list = Parallel(n_jobs=self.n_jobs)(
delayed(self._transform_case_single)(
X[i],
feature,
) for i in range(n_instances))
return np.asarray(c22_list)
def predict(self, X: NumpyOrDF, y=None) -> NumpyArray:
"""
Return cluster center index for data samples.
Parameters
----------
X: 2D np.array with shape (n_instances, n_timepoints)
or pd.DataFrame in nested format
panel of time series to cluster
y: ignored, exists for API consistency reasons
Returns
-------
Numpy_Array: 1D np.array of length n_instances
Index of the cluster each sample belongs to
"""
self.check_is_fitted()
if isinstance(X, pd.DataFrame):
X = from_nested_to_2d_array(X, return_numpy=True)
self._check_params(X)
return self._predict(X)
def fit(self, X: NumpyOrDF, y=None):
"""
Fit the clustering algorithm on the dataset X
Parameters
----------
X: 2D np.array with shape (n_instances, n_timepoints)
or pd.DataFrame in nested format
panel of univariate time series to train the clustering model on
y: ignored, exists for API consistency reasons
Returns
-------
reference to self
"""
if isinstance(X, pd.DataFrame):
X = from_nested_to_2d_array(X, return_numpy=True)
self._check_params(X)
self._fit(X)
self._is_fitted = True
return self
def transform_single_feature(self, X, feature, case_id=None):
"""Transform data into a specified catch22 feature.
Parameters
----------
X : pandas DataFrame, input time series.
feature : int, catch22 feature id or String, catch22 feature
name.
case_id : int, identifier for the current set of cases. If the case_id is not
None and the same as the previously used case_id, calculations from
previous features will be reused.
Returns
-------
Numpy array containing a catch22 feature for each input series.
"""
if isinstance(feature,
(int, np.integer)) or isinstance(feature,
(float, np.float)):
if feature > 21 or feature < 0:
raise ValueError("Invalid catch22 feature ID")
elif isinstance(feature, str):
if feature in feature_names:
feature = feature_names.index(feature)
else:
raise ValueError("Invalid catch22 feature name")
else:
raise ValueError("catch22 feature name or ID required")
if isinstance(X, pd.DataFrame):
X = from_nested_to_2d_array(X, return_numpy=True)
n_instances = X.shape[0]
X = np.reshape(X, (n_instances, -1))
series_length = X.shape[1]
if case_id is not None:
if case_id != self._case_id:
self._case_id = case_id
self._st_n_instances = n_instances
self._st_series_length = series_length
self._outlier_series = [None] * n_instances
self._smin = [None] * n_instances
self._smax = [None] * n_instances
self._smean = [None] * n_instances
self._fft = [None] * n_instances
self._ac = [None] * n_instances
self._acfz = [None] * n_instances
else:
if (n_instances != self._st_n_instances
or series_length != self._st_series_length):
raise ValueError(
"Catch22: case_is the same, but n_instances and "
"series_length do not match last seen for single "
"feature transform.")
c22_list = Parallel(n_jobs=self.n_jobs)(
delayed(self._transform_case_single)(
X[i],
feature,
case_id,
i,
) for i in range(n_instances))
if self.replace_nans:
c22_list = np.nan_to_num(c22_list, False, 0, 0, 0)
return np.asarray(c22_list)
