diff --git a/extreme_estimator/estimator/abstract_estimator.py b/extreme_estimator/estimator/abstract_estimator.py
index 3e22c0e27b5eeb7bd75d418861e8adb3265ae0f9..d5d36f14bfe6bbe3385baf49a55cc988731e56f0 100644
--- a/extreme_estimator/estimator/abstract_estimator.py
+++ b/extreme_estimator/estimator/abstract_estimator.py
@@ -1,6 +1,7 @@
import time
from spatio_temporal_dataset.dataset.abstract_dataset import AbstractDataset
+from spatio_temporal_dataset.dataset.spatio_temporal_split import SpatialTemporalSplit
class AbstractEstimator(object):
@@ -14,6 +15,7 @@ class AbstractEstimator(object):
def __init__(self, dataset: AbstractDataset):
self.dataset = dataset # type: AbstractDataset
self.additional_information = dict()
+ self.train_split = SpatialTemporalSplit.train
def fit(self):
ts = time.time()
diff --git a/extreme_estimator/estimator/full_estimator.py b/extreme_estimator/estimator/full_estimator.py
index aac5229c40fbf3d7460fabfa9ec1f9397d76520f..d60d1776dd506eb8a042139f60314744457d60d7 100644
--- a/extreme_estimator/estimator/full_estimator.py
+++ b/extreme_estimator/estimator/full_estimator.py
@@ -42,7 +42,7 @@ class SmoothMarginalsThenUnitaryMsp(AbstractFullEstimator):
# Estimate the margin parameters
self.margin_estimator.fit()
# Compute the maxima_frech
- maxima_gev_train = self.dataset.maxima_gev(split=SpatialTemporalSplit.train)
+ maxima_gev_train = self.dataset.maxima_gev(split=self.train_split)
maxima_frech = AbstractMarginModel.gev2frech(maxima_gev=maxima_gev_train,
coordinates_values=self.dataset.coordinates_values,
margin_function=self.margin_estimator.margin_function_fitted)
@@ -70,8 +70,8 @@ class FullEstimatorInASingleStepWithSmoothMargin(AbstractFullEstimator):
def _fit(self):
# Estimate both the margin and the max-stable structure
self.full_params_fitted = self.max_stable_model.fitmaxstab(
- maxima_gev=self.dataset.maxima_gev(split=SpatialTemporalSplit.train),
- df_coordinates=self.dataset.df_coordinates,
+ maxima_gev=self.dataset.maxima_gev(split=self.train_split),
+ df_coordinates=self.dataset.df_coordinates(split=self.train_split),
fit_marge=True,
fit_marge_form_dict=self.linear_margin_function_to_fit.form_dict,
margin_start_dict=self.linear_margin_function_to_fit.coef_dict
diff --git a/extreme_estimator/estimator/max_stable_estimator.py b/extreme_estimator/estimator/max_stable_estimator.py
index dff88648bc183ede968a87e43df2b92f9de63dc4..c1dac5177e8b6d944f45688c7a08b27f56d85fc1 100644
--- a/extreme_estimator/estimator/max_stable_estimator.py
+++ b/extreme_estimator/estimator/max_stable_estimator.py
@@ -20,8 +20,8 @@ class MaxStableEstimator(AbstractMaxStableEstimator):
def _fit(self):
assert self.dataset.maxima_frech(split=SpatialTemporalSplit.train) is not None
self.max_stable_params_fitted = self.max_stable_model.fitmaxstab(
- maxima_frech=self.dataset.maxima_frech(split=SpatialTemporalSplit.train),
- df_coordinates=self.dataset.coordinates.df_coordinates)
+ maxima_frech=self.dataset.maxima_frech(split=self.train_split),
+ df_coordinates=self.dataset.df_coordinates(split=self.train_split))
def _error(self, true_max_stable_params: dict):
absolute_errors = {param_name: np.abs(param_true_value - self.max_stable_params_fitted[param_name])
diff --git a/extreme_estimator/extreme_models/margin_model/margin_function/abstract_margin_function.py b/extreme_estimator/extreme_models/margin_model/margin_function/abstract_margin_function.py
index 293ae7a1601bc8cf3de816bcb426220e82521a2a..1ded02a97d6b959778b71b35088fb84e80ebdb3e 100644
--- a/extreme_estimator/extreme_models/margin_model/margin_function/abstract_margin_function.py
+++ b/extreme_estimator/extreme_models/margin_model/margin_function/abstract_margin_function.py
@@ -73,7 +73,7 @@ class AbstractMarginFunction(object):
# TODO: to avoid getting the value several times, I could cache the results
if self.dot_display:
resolution = len(self.coordinates)
- linspace = self.coordinates.coordinates_values[:, 0]
+ linspace = self.coordinates.coordinates_values()[:, 0]
print('dot display')
else:
resolution = 100
diff --git a/extreme_estimator/extreme_models/margin_model/margin_function/linear_margin_function.py b/extreme_estimator/extreme_models/margin_model/margin_function/linear_margin_function.py
index 2d50202ea120a1e0c9aa7157a0b5af05dfe10a33..c3b5b96b3f12a9a964c22651fd856fdd8636d945 100644
--- a/extreme_estimator/extreme_models/margin_model/margin_function/linear_margin_function.py
+++ b/extreme_estimator/extreme_models/margin_model/margin_function/linear_margin_function.py
@@ -48,7 +48,7 @@ class LinearMarginFunction(IndependentMarginFunction):
# Otherwise, we fit a LinearParamFunction
else:
param_function = LinearParamFunction(linear_dims=self.gev_param_name_to_linear_dims[gev_param_name],
- coordinates=self.coordinates.coordinates_values,
+ coordinates=self.coordinates.coordinates_values(),
linear_coef=linear_coef)
# Add the param_function to the dictionary
self.gev_param_name_to_param_function[gev_param_name] = param_function
diff --git a/extreme_estimator/extreme_models/max_stable_model/abstract_max_stable_model.py b/extreme_estimator/extreme_models/max_stable_model/abstract_max_stable_model.py
index f9e1235caa8ac9f2c9ded2bb63d55a8797392125..41303c9e5c9ed8d81e7c918887a73bb843c0f5ae 100644
--- a/extreme_estimator/extreme_models/max_stable_model/abstract_max_stable_model.py
+++ b/extreme_estimator/extreme_models/max_stable_model/abstract_max_stable_model.py
@@ -22,7 +22,8 @@ class AbstractMaxStableModel(AbstractModel):
def cov_mod_param(self):
return {'cov.mod': self.cov_mod}
- def fitmaxstab(self, df_coordinates: pd.DataFrame, maxima_frech: np.ndarray=None, maxima_gev: np.ndarray=None, fit_marge=False,
+ def fitmaxstab(self, df_coordinates: pd.DataFrame, maxima_frech: np.ndarray = None, maxima_gev: np.ndarray = None,
+ fit_marge=False,
fit_marge_form_dict=None, margin_start_dict=None) -> dict:
assert isinstance(df_coordinates, pd.DataFrame)
if fit_marge:
@@ -32,6 +33,10 @@ class AbstractMaxStableModel(AbstractModel):
# Prepare the data
maxima = maxima_gev if fit_marge else maxima_frech
assert isinstance(maxima, np.ndarray)
+ assert len(df_coordinates) == len(maxima), 'Coordinates and observations sizes should match,' \
+ 'check that the same split was used for both objects \n,' \
+ 'df_coordinates size: {}, data size {}'.format(len(df_coordinates),
+ len(maxima))
data = np.transpose(maxima)
# Prepare the coord
@@ -75,12 +80,12 @@ class AbstractMaxStableModel(AbstractModel):
fitted_values = {key: fitted_values.rx2(key)[0] for key in fitted_values.names}
return fitted_values
- def rmaxstab(self, nb_obs: int, coordinates: np.ndarray) -> np.ndarray:
+ def rmaxstab(self, nb_obs: int, coordinates_values: np.ndarray) -> np.ndarray:
"""
Return an numpy of maxima. With rows being the stations and columns being the years of maxima
"""
maxima_frech = np.array(
- r.rmaxstab(nb_obs, coordinates, *list(self.cov_mod_param.values()), **self.params_sample))
+ r.rmaxstab(nb_obs, coordinates_values, *list(self.cov_mod_param.values()), **self.params_sample))
return np.transpose(maxima_frech)
def remove_unused_parameters(self, start_dict, coordinate_dim):
diff --git a/spatio_temporal_dataset/coordinates/abstract_coordinates.py b/spatio_temporal_dataset/coordinates/abstract_coordinates.py
index dc18803bd359032720de6f1eaf64d7c7589045e5..4cf3fd6f61086593914555273d3113f2e3e90230 100644
--- a/spatio_temporal_dataset/coordinates/abstract_coordinates.py
+++ b/spatio_temporal_dataset/coordinates/abstract_coordinates.py
@@ -1,11 +1,14 @@
import os.path as op
from typing import List
+import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
-import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
+from spatio_temporal_dataset.dataset.spatio_temporal_split import s_split_from_ratio, TEST_SPLIT_STR, \
+ TRAIN_SPLIT_STR, train_ind_from_s_split, SpatialTemporalSplit
+
class AbstractCoordinates(object):
# Columns
@@ -14,45 +17,54 @@ class AbstractCoordinates(object):
COORDINATE_Z = 'coord_z'
COORDINATE_NAMES = [COORDINATE_X, COORDINATE_Y, COORDINATE_Z]
COORDINATE_SPLIT = 'coord_split'
- # Constants for the split column
- TRAIN_SPLIT_STR = 'train_split'
- TEST_SPLIT_STR = 'test_split'
- def __init__(self, df_coordinates: pd.DataFrame, s_split: pd.Series = None):
- self.df_coordinates = df_coordinates # type: pd.DataFrame
+ def __init__(self, df_coord: pd.DataFrame, s_split: pd.Series = None):
+ self.df_coord = df_coord # type: pd.DataFrame
self.s_split = s_split # type: pd.Series
# ClassMethod constructor
@classmethod
- def from_df(cls, df: pd.DataFrame):
+ def from_df(cls, df: pd.DataFrame, train_split_ratio: float = None):
# X and coordinates must be defined
assert cls.COORDINATE_X in df.columns
- df_coordinates = df.loc[:, cls.coordinates_columns(df)]
- # Potentially, a split column can be specified
- s_split = df[cls.COORDINATE_SPLIT] if cls.COORDINATE_SPLIT in df.columns else None
- if s_split is not None:
- assert s_split.isin([cls.TRAIN_SPLIT_STR, cls.TEST_SPLIT_STR])
- return cls(df_coordinates=df_coordinates, s_split=s_split)
+ # Create a split based on the train_split_ratio
+ if train_split_ratio is not None:
+ assert cls.COORDINATE_SPLIT not in df.columns, "A split has already been defined"
+ s_split = s_split_from_ratio(length=len(df), train_split_ratio=train_split_ratio)
+ df[cls.COORDINATE_SPLIT] = s_split
+ # Potentially, a split column can be specified directly in df
+ if cls.COORDINATE_SPLIT not in df.columns:
+ df_coord = df
+ s_split = None
+ else:
+ df_coord = df.loc[:, cls.coordinates_columns(df)]
+ s_split = df[cls.COORDINATE_SPLIT]
+ assert s_split.isin([TRAIN_SPLIT_STR, TEST_SPLIT_STR]).all()
+ return cls(df_coord=df_coord, s_split=s_split)
@classmethod
def from_csv(cls, csv_path: str = None):
assert csv_path is not None
assert op.exists(csv_path)
df = pd.read_csv(csv_path)
+ # Index correspond to the first column
+ index_column_name = df.columns[0]
+ assert index_column_name not in cls.coordinates_columns(df)
+ df.set_index(index_column_name, inplace=True)
return cls.from_df(df)
@classmethod
- def from_nb_points(cls, nb_points: int, **kwargs):
+ def from_nb_points(cls, nb_points: int, train_split_ratio: float = None, **kwargs):
# Call the default class method from csv
coordinates = cls.from_csv() # type: AbstractCoordinates
- # Sample randomly nb_points coordinates
+ # Check that nb_points asked is not superior to the number of coordinates
nb_coordinates = len(coordinates)
if nb_points > nb_coordinates:
raise Exception('Nb coordinates in csv: {} < Nb points desired: {}'.format(nb_coordinates, nb_points))
- else:
- df_sample = pd.DataFrame.sample(coordinates.df, n=nb_points)
- return cls.from_df(df=df_sample)
+ # Sample randomly nb_points coordinates
+ df_sample = pd.DataFrame.sample(coordinates.df_merged, n=nb_points)
+ return cls.from_df(df=df_sample, train_split_ratio=train_split_ratio)
@classmethod
def coordinates_columns(cls, df_coord: pd.DataFrame) -> List[str]:
@@ -64,52 +76,48 @@ class AbstractCoordinates(object):
@property
def columns(self):
- return self.coordinates_columns(df_coord=self.df_coordinates)
+ return self.coordinates_columns(df_coord=self.df_coord)
@property
def nb_columns(self):
return len(self.columns)
@property
- def df(self) -> pd.DataFrame:
- # Merged DataFrame of df_coord and s_split
- return self.df_coordinates if self.s_split is None else self.df_coordinates.join(self.s_split)
+ def index(self):
+ return self.df_coord.index
- def df_coordinates_split(self, split_str: str) -> pd.DataFrame:
- assert self.s_split is not None
- ind = self.s_split == split_str
- return self.df_coordinates.loc[ind]
+ @property
+ def df_merged(self) -> pd.DataFrame:
+ # Merged DataFrame of df_coord and s_split
+ return self.df_coord if self.s_split is None else self.df_coord.join(self.s_split)
- def _coordinates_values(self, df_coordinates: pd.DataFrame) -> np.ndarray:
- return df_coordinates.loc[:, self.coordinates_columns(df_coordinates)].values
+ def df_coordinates(self, split: SpatialTemporalSplit = SpatialTemporalSplit.all) -> pd.DataFrame:
+ if split is SpatialTemporalSplit.all or self.s_split is None:
+ return self.df_coord
+ elif split in [SpatialTemporalSplit.train, SpatialTemporalSplit.test_temporal]:
+ return self.df_coord.loc[self.train_ind]
+ else:
+ return self.df_coord.loc[~self.train_ind]
- @property
- def coordinates_values(self) -> np.ndarray:
- return self._coordinates_values(df_coordinates=self.df_coordinates)
+ def coordinates_values(self, split: SpatialTemporalSplit = SpatialTemporalSplit.all) -> np.ndarray:
+ return self.df_coordinates(split).values
@property
def x_coordinates(self) -> np.ndarray:
- return self.df_coordinates.loc[:, self.COORDINATE_X].values.copy()
+ return self.df_coord[self.COORDINATE_X].values.copy()
@property
def y_coordinates(self) -> np.ndarray:
- return self.df_coordinates.loc[:, self.COORDINATE_Y].values.copy()
-
- @property
- def index(self) -> pd.Series:
- return self.df_coordinates.index
+ return self.df_coord[self.COORDINATE_Y].values.copy()
@property
def train_ind(self) -> pd.Series:
- if self.s_split is None:
- return None
- else:
- return self.s_split.isin([self.TRAIN_SPLIT_STR])
+ return train_ind_from_s_split(s_split=self.s_split)
# Visualization
def visualize(self):
- nb_coordinates_columns = len(self.coordinates_columns(self.df_coordinates))
+ nb_coordinates_columns = len(self.coordinates_columns(self.df_coord))
if nb_coordinates_columns == 1:
self.visualization_1D()
elif nb_coordinates_columns == 2:
@@ -118,21 +126,23 @@ class AbstractCoordinates(object):
self.visualization_3D()
def visualization_1D(self):
- assert len(self.coordinates_columns(self.df_coordinates)) >= 1
- x = self.coordinates_values[:]
+ assert len(self.coordinates_columns(self.df_coord)) >= 1
+ x = self.coordinates_values()[:]
y = np.zeros(len(x))
plt.scatter(x, y)
plt.show()
def visualization_2D(self):
- assert len(self.coordinates_columns(self.df_coordinates)) >= 2
- x, y = self.coordinates_values[:, 0], self.coordinates_values[:, 1]
+ assert len(self.coordinates_columns(self.df_coord)) >= 2
+ coordinates_values = self.coordinates_values()
+ x, y = coordinates_values[:, 0], coordinates_values[:, 1]
plt.scatter(x, y)
plt.show()
def visualization_3D(self):
- assert len(self.coordinates_columns(self.df_coordinates)) == 3
- x, y, z = self.coordinates_values[:, 0], self.coordinates_values[:, 1], self.coordinates_values[:, 2]
+ assert len(self.coordinates_columns(self.df_coord)) == 3
+ coordinates_values = self.coordinates_values()
+ x, y, z = coordinates_values[:, 0], coordinates_values[:, 1], coordinates_values[:, 2]
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d') # type: Axes3D
ax.scatter(x, y, z, marker='^')
@@ -141,10 +151,10 @@ class AbstractCoordinates(object):
# Magic Methods
def __len__(self):
- return len(self.df_coordinates)
+ return len(self.df_coord)
def __mul__(self, other: float):
- self.df_coordinates *= other
+ self.df_coord *= other
return self
def __rmul__(self, other):
diff --git a/spatio_temporal_dataset/coordinates/spatial_coordinates/alps_station_2D_coordinates.py b/spatio_temporal_dataset/coordinates/spatial_coordinates/alps_station_2D_coordinates.py
index 629e24f94824abbef266000689f7da7ea55bf922..a69b43f9b5890968c1fa05529de4ea056d072dab 100644
--- a/spatio_temporal_dataset/coordinates/spatial_coordinates/alps_station_2D_coordinates.py
+++ b/spatio_temporal_dataset/coordinates/spatial_coordinates/alps_station_2D_coordinates.py
@@ -10,7 +10,7 @@ class AlpsStation2DCoordinates(AlpsStation3DCoordinates):
def from_csv(cls, csv_file='coord-lambert2'):
# Remove the Z coordinates from df_coord
spatial_coordinates = super().from_csv(csv_file) # type: AlpsStation3DCoordinates
- spatial_coordinates.df_coordinates.drop(cls.COORDINATE_Z, axis=1, inplace=True)
+ spatial_coordinates.df_coord.drop(cls.COORDINATE_Z, axis=1, inplace=True)
return spatial_coordinates
diff --git a/spatio_temporal_dataset/coordinates/spatial_coordinates/alps_station_3D_coordinates.py b/spatio_temporal_dataset/coordinates/spatial_coordinates/alps_station_3D_coordinates.py
index d5ef2ad10d125cda6db89b5e5469032c02cd6cb7..6d00efb4f2d3bf609aaa2a25702de2927113b750 100644
--- a/spatio_temporal_dataset/coordinates/spatial_coordinates/alps_station_3D_coordinates.py
+++ b/spatio_temporal_dataset/coordinates/spatial_coordinates/alps_station_3D_coordinates.py
@@ -43,5 +43,6 @@ class AlpsStation3DCoordinatesWithAnisotropy(AlpsStation3DCoordinates):
@classmethod
def from_csv(cls, csv_file='coord-lambert2'):
coord = super().from_csv(csv_file)
+ print(coord)
return TransformedCoordinates.from_coordinates(coordinates=coord,
transformation_function=AnisotropyTransformation())
diff --git a/spatio_temporal_dataset/coordinates/spatial_coordinates/generated_spatial_coordinates.py b/spatio_temporal_dataset/coordinates/spatial_coordinates/generated_spatial_coordinates.py
index 267d7feed74bc79a226e462496a3732554349729..fc600e268fff3bd5a20f6dd4f004b5bfcf3a84f9 100644
--- a/spatio_temporal_dataset/coordinates/spatial_coordinates/generated_spatial_coordinates.py
+++ b/spatio_temporal_dataset/coordinates/spatial_coordinates/generated_spatial_coordinates.py
@@ -10,13 +10,13 @@ import matplotlib.pyplot as plt
class CircleCoordinates(AbstractCoordinates):
@classmethod
- def from_nb_points(cls, nb_points, max_radius=1.0):
+ def from_nb_points(cls, nb_points, train_split_ratio: float = None, max_radius=1.0):
# Sample uniformly inside the circle
angles = np.array(r.runif(nb_points, max=2 * math.pi))
radius = np.sqrt(np.array(r.runif(nb_points, max=max_radius)))
df = pd.DataFrame.from_dict({cls.COORDINATE_X: radius * np.cos(angles),
cls.COORDINATE_Y: radius * np.sin(angles)})
- return cls.from_df(df)
+ return cls.from_df(df, train_split_ratio)
def visualization_2D(self):
r = 1.0
@@ -30,6 +30,6 @@ class CircleCoordinates(AbstractCoordinates):
class CircleCoordinatesRadius2(CircleCoordinates):
@classmethod
- def from_nb_points(cls, nb_points, max_radius=1.0):
- return 2 * super().from_nb_points(nb_points, max_radius)
+ def from_nb_points(cls, nb_points, train_split_ratio: float = None, max_radius=1.0):
+ return 2 * super().from_nb_points(nb_points, train_split_ratio, max_radius)
diff --git a/spatio_temporal_dataset/coordinates/transformed_coordinates/transformed_coordinates.py b/spatio_temporal_dataset/coordinates/transformed_coordinates/transformed_coordinates.py
index 4cc302d98d3854f889274d14f626927c3e10996e..5022b24416882cea801720b915cd7e8ed470d703 100644
--- a/spatio_temporal_dataset/coordinates/transformed_coordinates/transformed_coordinates.py
+++ b/spatio_temporal_dataset/coordinates/transformed_coordinates/transformed_coordinates.py
@@ -7,8 +7,8 @@ class TransformedCoordinates(AbstractCoordinates):
@classmethod
def from_coordinates(cls, coordinates: AbstractCoordinates,
transformation_function: AbstractTransformation):
- df_coordinates_transformed = coordinates.df_coordinates.copy()
+ df_coordinates_transformed = coordinates.df_coord.copy()
df_coordinates_transformed = transformation_function.transform(df_coord=df_coordinates_transformed)
- return cls(df_coordinates=df_coordinates_transformed, s_split=coordinates.s_split)
+ return cls(df_coord=df_coordinates_transformed, s_split=coordinates.s_split)
diff --git a/spatio_temporal_dataset/coordinates/unidimensional_coordinates/coordinates_1D.py b/spatio_temporal_dataset/coordinates/unidimensional_coordinates/coordinates_1D.py
index 34dbacc35181cd5b443fa910cdd04abc01d97c7f..64c56d52f34535df83b137cfb35cdb52dbd7949d 100644
--- a/spatio_temporal_dataset/coordinates/unidimensional_coordinates/coordinates_1D.py
+++ b/spatio_temporal_dataset/coordinates/unidimensional_coordinates/coordinates_1D.py
@@ -13,17 +13,17 @@ class AbstractUniDimensionalCoordinates(AbstractCoordinates):
class LinSpaceCoordinates(AbstractUniDimensionalCoordinates):
@classmethod
- def from_nb_points(cls, nb_points, start=-1.0, end=1.0):
+ def from_nb_points(cls, nb_points, train_split_ratio: float = None, start=-1.0, end=1.0):
axis_coordinates = np.linspace(start, end, nb_points)
df = pd.DataFrame.from_dict({cls.COORDINATE_X: axis_coordinates})
- return cls.from_df(df)
+ return cls.from_df(df, train_split_ratio)
class UniformCoordinates(AbstractUniDimensionalCoordinates):
@classmethod
- def from_nb_points(cls, nb_points, start=-1.0, end=1.0):
+ def from_nb_points(cls, nb_points, train_split_ratio: float = None, start=-1.0, end=1.0):
# Sample uniformly inside the circle
axis_coordinates = np.array(r.runif(nb_points, min=start, max=end))
df = pd.DataFrame.from_dict({cls.COORDINATE_X: axis_coordinates})
- return cls.from_df(df)
+ return cls.from_df(df, train_split_ratio)
diff --git a/spatio_temporal_dataset/dataset/abstract_dataset.py b/spatio_temporal_dataset/dataset/abstract_dataset.py
index f17d9610e1e95f75a4773d4688fd20c9c6558391..ca1feef99438ffacfe057d6a7293469ebbe6a9c8 100644
--- a/spatio_temporal_dataset/dataset/abstract_dataset.py
+++ b/spatio_temporal_dataset/dataset/abstract_dataset.py
@@ -35,15 +35,15 @@ class AbstractDataset(object):
@property
def df_dataset(self) -> pd.DataFrame:
# Merge dataframes with the maxima and with the coordinates
- return self.temporal_observations.df_maxima_gev.join(self.coordinates.df_coordinates)
+ # todo: maybe I should add the split from the temporal observations
+ return self.temporal_observations.df_maxima_gev.join(self.coordinates.df_merged)
- @property
- def df_coordinates(self):
- return self.coordinates.df_coordinates
+ def df_coordinates(self, split: SpatialTemporalSplit = SpatialTemporalSplit.all):
+ return self.coordinates.df_coordinates(split=split)
@property
- def coordinates_values(self):
- return self.coordinates.coordinates_values
+ def coordinates_values(self, split: SpatialTemporalSplit = SpatialTemporalSplit.all):
+ return self.coordinates.coordinates_values(split=split)
def maxima_gev(self, split: SpatialTemporalSplit = SpatialTemporalSplit.all) -> np.ndarray:
return self.temporal_observations.maxima_gev(split, self.spatio_temporal_slicer)
diff --git a/spatio_temporal_dataset/dataset/simulation_dataset.py b/spatio_temporal_dataset/dataset/simulation_dataset.py
index a635baa7f40bb61ff39e2a8f35c67ed615d99e4d..c7140a7ef70551d25b3e237db4866f9dae210505 100644
--- a/spatio_temporal_dataset/dataset/simulation_dataset.py
+++ b/spatio_temporal_dataset/dataset/simulation_dataset.py
@@ -26,16 +26,18 @@ class SimulatedDataset(AbstractDataset):
class MaxStableDataset(SimulatedDataset):
@classmethod
- def from_sampling(cls, nb_obs: int, max_stable_model: AbstractMaxStableModel, coordinates: AbstractCoordinates):
- temporal_obs = MaxStableAnnualMaxima.from_sampling(nb_obs, max_stable_model, coordinates)
+ def from_sampling(cls, nb_obs: int, max_stable_model: AbstractMaxStableModel, coordinates: AbstractCoordinates,
+ train_split_ratio: float = None):
+ temporal_obs = MaxStableAnnualMaxima.from_sampling(nb_obs, max_stable_model, coordinates, train_split_ratio)
return cls(temporal_observations=temporal_obs, coordinates=coordinates, max_stable_model=max_stable_model)
class MarginDataset(SimulatedDataset):
@classmethod
- def from_sampling(cls, nb_obs: int, margin_model: AbstractMarginModel, coordinates: AbstractCoordinates):
- temporal_obs = MarginAnnualMaxima.from_sampling(nb_obs, coordinates, margin_model)
+ def from_sampling(cls, nb_obs: int, margin_model: AbstractMarginModel, coordinates: AbstractCoordinates,
+ train_split_ratio: float = None):
+ temporal_obs = MarginAnnualMaxima.from_sampling(nb_obs, coordinates, margin_model, train_split_ratio)
return cls(temporal_observations=temporal_obs, coordinates=coordinates, margin_model=margin_model)
@@ -44,8 +46,9 @@ class FullSimulatedDataset(SimulatedDataset):
@classmethod
def from_double_sampling(cls, nb_obs: int, max_stable_model: AbstractMaxStableModel,
coordinates: AbstractCoordinates,
- margin_model: AbstractMarginModel):
+ margin_model: AbstractMarginModel,
+ train_split_ratio: float = None):
temporal_obs = FullAnnualMaxima.from_double_sampling(nb_obs, max_stable_model,
- coordinates, margin_model)
+ coordinates, margin_model, train_split_ratio)
return cls(temporal_observations=temporal_obs, coordinates=coordinates, max_stable_model=max_stable_model,
margin_model=margin_model)
diff --git a/spatio_temporal_dataset/dataset/spatio_temporal_split.py b/spatio_temporal_dataset/dataset/spatio_temporal_split.py
index 599e656db35b1aea19c2b48446ebd1c8560469c2..3d7d210aa9ed8d3330b451df0a2384705c967cac 100644
--- a/spatio_temporal_dataset/dataset/spatio_temporal_split.py
+++ b/spatio_temporal_dataset/dataset/spatio_temporal_split.py
@@ -17,7 +17,9 @@ class SpatioTemporalSlicer(object):
self.index_train_ind = coordinate_train_ind # type: pd.Series
self.column_train_ind = observation_train_ind # type: pd.Series
if self.ind_are_not_defined:
- assert self.index_train_ind is None and self.column_train_ind is None, "One split was not defined"
+ msg = "One split was not defined \n \n" \
+ "index: \n {} \n, column:\n {} \n".format(self.index_train_ind, self.column_train_ind)
+ assert self.index_train_ind is None and self.column_train_ind is None, msg
@property
def index_test_ind(self) -> pd.Series:
@@ -36,8 +38,8 @@ class SpatioTemporalSlicer(object):
# By default, if one of the two split is not defined we return all the data
if self.ind_are_not_defined or split is SpatialTemporalSplit.all:
return df
- assert df.columns == self.column_train_ind.index
- assert df.index == self.index_train_ind.index
+ assert pd.RangeIndex.equals(df.columns, self.column_train_ind.index)
+ assert pd.RangeIndex.equals(df.index, self.index_train_ind.index)
if split is SpatialTemporalSplit.train:
return df.loc[self.index_train_ind, self.column_train_ind]
elif split is SpatialTemporalSplit.test:
@@ -46,3 +48,29 @@ class SpatioTemporalSlicer(object):
return df.loc[self.index_test_ind, self.column_train_ind]
elif split is SpatialTemporalSplit.test_temporal:
return df.loc[self.index_train_ind, self.column_test_ind]
+
+
+SPLIT_NAME = 'split'
+TRAIN_SPLIT_STR = 'train_split'
+TEST_SPLIT_STR = 'test_split'
+
+
+def train_ind_from_s_split(s_split):
+ """
+
+ :param s_split:
+ :return:
+ """
+ if s_split is None:
+ return None
+ else:
+ return s_split.isin([TRAIN_SPLIT_STR])
+
+
+def s_split_from_ratio(length, train_split_ratio):
+ assert 0 < train_split_ratio < 1
+ s = pd.Series([TEST_SPLIT_STR for _ in range(length)])
+ nb_points_train = int(length * train_split_ratio)
+ train_ind = pd.Series.sample(s, n=nb_points_train).index
+ s.loc[train_ind] = TRAIN_SPLIT_STR
+ return s
diff --git a/spatio_temporal_dataset/temporal_observations/abstract_temporal_observations.py b/spatio_temporal_dataset/temporal_observations/abstract_temporal_observations.py
index 8a19a12307639f23a5a3627f16731520f31ed56d..86926ce1f61019ed4d6f70d0c000d599d279f03d 100644
--- a/spatio_temporal_dataset/temporal_observations/abstract_temporal_observations.py
+++ b/spatio_temporal_dataset/temporal_observations/abstract_temporal_observations.py
@@ -1,35 +1,45 @@
import pandas as pd
import numpy as np
-from spatio_temporal_dataset.dataset.spatio_temporal_split import SpatialTemporalSplit, SpatioTemporalSlicer
+from spatio_temporal_dataset.dataset.spatio_temporal_split import SpatialTemporalSplit, SpatioTemporalSlicer, \
+ train_ind_from_s_split, TEST_SPLIT_STR, TRAIN_SPLIT_STR, s_split_from_ratio
class AbstractTemporalObservations(object):
- # Constants for the split column
- TRAIN_SPLIT_STR = 'train_split'
- TEST_SPLIT_STR = 'test_split'
-
def __init__(self, df_maxima_frech: pd.DataFrame = None, df_maxima_gev: pd.DataFrame = None,
- s_split: pd.Series = None):
+ s_split: pd.Series = None, train_split_ratio: float = None):
"""
Main attribute of the class is the DataFrame df_maxima
Index are stations index
Columns are the temporal moment of the maxima
"""
- if s_split is not None:
- assert s_split.isin([self.TRAIN_SPLIT_STR, self.TEST_SPLIT_STR])
- self.s_split = s_split
+ assert df_maxima_frech is not None or df_maxima_gev is not None
self.df_maxima_frech = df_maxima_frech
self.df_maxima_gev = df_maxima_gev
+ if s_split is not None and train_split_ratio is not None:
+ raise AttributeError('A split is already defined, there is no need to specify a ratio')
+ elif s_split is not None or train_split_ratio is not None:
+ if train_split_ratio:
+ s_split = s_split_from_ratio(length=self.nb_obs, train_split_ratio=train_split_ratio)
+ assert s_split.isin([TRAIN_SPLIT_STR, TEST_SPLIT_STR]).all()
+ self.s_split = s_split
+
+ @property
+ def nb_obs(self):
+ if self.df_maxima_frech is not None:
+ return len(self.df_maxima_frech.columns)
+ else:
+ return len(self.df_maxima_gev.columns)
+
@classmethod
def from_df(cls, df):
pass
@staticmethod
def df_maxima(df: pd.DataFrame, split: SpatialTemporalSplit = SpatialTemporalSplit.all,
- slicer: SpatioTemporalSlicer = None):
+ slicer: SpatioTemporalSlicer = None) -> pd.DataFrame:
if slicer is None:
assert split is SpatialTemporalSplit.all
return df
@@ -49,7 +59,4 @@ class AbstractTemporalObservations(object):
@property
def train_ind(self) -> pd.Series:
- if self.s_split is None:
- return None
- else:
- return self.s_split.isin([self.TRAIN_SPLIT_STR])
+ return train_ind_from_s_split(s_split=self.s_split)
diff --git a/spatio_temporal_dataset/temporal_observations/annual_maxima_observations.py b/spatio_temporal_dataset/temporal_observations/annual_maxima_observations.py
index a197687c57b32592ad3cb59fbda5d277342b1c19..ecb86156f573ca0d9cc15692aee16de9f0c57c0a 100644
--- a/spatio_temporal_dataset/temporal_observations/annual_maxima_observations.py
+++ b/spatio_temporal_dataset/temporal_observations/annual_maxima_observations.py
@@ -3,6 +3,7 @@ import pandas as pd
from extreme_estimator.extreme_models.margin_model.abstract_margin_model import AbstractMarginModel
from extreme_estimator.extreme_models.max_stable_model.abstract_max_stable_model import AbstractMaxStableModel
from spatio_temporal_dataset.coordinates.abstract_coordinates import AbstractCoordinates
+from spatio_temporal_dataset.dataset.spatio_temporal_split import s_split_from_ratio
from spatio_temporal_dataset.temporal_observations.abstract_temporal_observations import AbstractTemporalObservations
@@ -18,29 +19,31 @@ class MarginAnnualMaxima(AnnualMaxima):
@classmethod
def from_sampling(cls, nb_obs: int, coordinates: AbstractCoordinates,
- margin_model: AbstractMarginModel):
- maxima_gev = margin_model.rmargin_from_nb_obs(nb_obs=nb_obs, coordinates_values=coordinates.coordinates_values)
+ margin_model: AbstractMarginModel, train_split_ratio: float = None):
+ maxima_gev = margin_model.rmargin_from_nb_obs(nb_obs=nb_obs, coordinates_values=coordinates.coordinates_values())
df_maxima_gev = pd.DataFrame(data=maxima_gev, index=coordinates.index)
- return cls(df_maxima_gev=df_maxima_gev)
+ return cls(df_maxima_gev=df_maxima_gev, train_split_ratio=train_split_ratio)
-class MaxStableAnnualMaxima(AbstractTemporalObservations):
+class MaxStableAnnualMaxima(AnnualMaxima):
@classmethod
- def from_sampling(cls, nb_obs: int, max_stable_model: AbstractMaxStableModel, coordinates: AbstractCoordinates):
- maxima_frech = max_stable_model.rmaxstab(nb_obs=nb_obs, coordinates=coordinates.coordinates_values)
+ def from_sampling(cls, nb_obs: int, max_stable_model: AbstractMaxStableModel, coordinates: AbstractCoordinates,
+ train_split_ratio: float = None):
+ maxima_frech = max_stable_model.rmaxstab(nb_obs=nb_obs, coordinates_values=coordinates.coordinates_values())
df_maxima_frech = pd.DataFrame(data=maxima_frech, index=coordinates.index)
- return cls(df_maxima_frech=df_maxima_frech)
+ return cls(df_maxima_frech=df_maxima_frech, train_split_ratio=train_split_ratio)
class FullAnnualMaxima(MaxStableAnnualMaxima):
@classmethod
def from_double_sampling(cls, nb_obs: int, max_stable_model: AbstractMaxStableModel,
- coordinates: AbstractCoordinates, margin_model: AbstractMarginModel):
- max_stable_annual_maxima = super().from_sampling(nb_obs, max_stable_model, coordinates)
+ coordinates: AbstractCoordinates, margin_model: AbstractMarginModel,
+ train_split_ratio: float = None):
+ max_stable_annual_maxima = super().from_sampling(nb_obs, max_stable_model, coordinates, train_split_ratio)
# Compute df_maxima_gev from df_maxima_frech
maxima_gev = margin_model.rmargin_from_maxima_frech(maxima_frech=max_stable_annual_maxima.maxima_frech(),
- coordinates_values=coordinates.coordinates_values)
+ coordinates_values=coordinates.coordinates_values())
max_stable_annual_maxima.df_maxima_gev = pd.DataFrame(data=maxima_gev, index=coordinates.index)
return max_stable_annual_maxima