<reponame>crazysal/chemml
import sys
import types
import numpy as np
import pandas as pd
from ....data.materials.CompositionEntry import CompositionEntry
from ....data.materials.util.LookUpData import LookUpData

class ElementalPropertyAttributeGenerator:
    """Class to set up and generate descriptors based on elemental property
    statistics.

    Computes the mean, maximum, minimum, range, mode and mean
    absolute deviation of all elemental properties provided.

    Attributes
    ----------
    elemental_properties : array-like
        Elemental properties to be associated with this class for the
        generation of features.
    lookup-data : dict
        Dictionary containing the property name as the key and a list of
        floats as the value.

    """

    # Initialize to an empty list.
    elemental_properties = []

    def __init__(self, use_default_properties=True):
        """Class constructor.

        Parameters
        ----------
        use_default_properties : bool
            Flag to use default set of properties as defined below.

        """

        # Use default properties to reproduce Ward et al. descriptor values.
        if (use_default_properties):
            self.elemental_properties = ["Number", "MendeleevNumber",
                                         "AtomicWeight", "MeltingT",
                                         "Column", "Row", "CovalentRadius",
                                         "Electronegativity", "NsValence",
                                         "NpValence", "NdValence",
                                         "NfValence", "NValance",
                                         "NsUnfilled", "NpUnfilled",
                                         "NdUnfilled", "NfUnfilled",
                                         "NUnfilled", "GSvolume_pa",
                                         "GSbandgap", "GSmagmom",
                                         "SpaceGroupNumber"]

        # Initialize dictionary that will contain all the property values.
        self.lookup_data = {}

    def load_lookup_data(self):
        """Function to load the property values into `self.lookup_data` for the
        computation of features.

        """

        self.lookup_data = LookUpData.load_properties(
            self.elemental_properties)

    def generate_features(self, entries):
        """Function to generate features as mentioned in the class description.

        Parameters
        ----------
        entries : array-like
            Compositions for which features are to be generated. A list of
            CompositionEntry's.

        Returns
        ----------
        features : DataFrame
            Features for the given entries. Pandas data frame containing the
            names and values of the descriptors.

        Raises
        ------
        ValueError
            If no elemental properties are set.
            If input is not of type list.
            If items in the list are not CompositionEntry instances.

        """

        # Initialize lists of feature values and headers for pandas data frame.
        feat_values = []
        feat_headers = []

        # Make sure that there is at least one elemental property provided.
        if not self.elemental_properties:
            raise ValueError("No elemental property is set. Add at least one "
                             "property to compute meaningful descriptors.")

        # If the dictionary containing the property values is empty,
        # load values into it.
        if not self.lookup_data:
            self.load_lookup_data()

        # Raise exception if input argument is not of type list of
        # CompositionEntry's.
        if not isinstance(entries, list):
            raise ValueError("Argument should be of type list of "
                             "CompositionEntry's")
        elif (entries and not isinstance(entries[0], CompositionEntry)):
            raise ValueError("Argument should be of type list of "
                             "CompositionEntry's")

        # Insert header names here.
        n_statistics = 6
        for prop in self.elemental_properties:
            feat_headers.append("mean_"+prop)
            feat_headers.append("maxdiff_" + prop)
            feat_headers.append("dev_" + prop)
            feat_headers.append("max_" + prop)
            feat_headers.append("min_" + prop)
            feat_headers.append("most_" + prop)

        missing_data = {}
        # Generate features for each entry.
        for entry in entries:
            elem_ids = entry.get_element_ids()
            elem_fractions = entry.get_element_fractions()
            max_f = max(elem_fractions)
            tmp_list = []
            # Look up values for each property.
            for prop in self.elemental_properties:
                tmp_prop = []
                for elem_id in elem_ids:
                    tmp_prop_value = self.lookup_data[prop][elem_id]
                    # If data is missing, make a note of it so that we can
                    # inform the user later.
                    if np.isnan(tmp_prop_value):
                        if not prop in missing_data:
                            missing_data[prop] = []
                        if elem_id not in missing_data[prop]:
                            missing_data[prop].append(elem_id)
                    tmp_prop.append(tmp_prop_value)

                # If there is no missing data, compute statistics.
                if not np.isnan(tmp_prop).any():
                    mean_ = np.average(tmp_prop, weights=elem_fractions)
                    max_ = max(tmp_prop)
                    min_= min(tmp_prop)
                    max_diff_ = max_ - min_
                    avg_dev_ = np.average([abs(x - mean_) for x in tmp_prop],
                                          weights=elem_fractions)

                    indices = [i for i,f in enumerate(elem_fractions) if f >=
                               max_f]
                    most_ = sum(tmp_prop[i] for i in indices)/len(indices)

                    tmp_list.append(mean_)
                    tmp_list.append(max_diff_)
                    tmp_list.append(avg_dev_)
                    tmp_list.append(max_)
                    tmp_list.append(min_)
                    tmp_list.append(most_)
                else:
                    # Handle nan descriptors here from missing data.
                    for i in range(n_statistics):
                        tmp_list.append(np.nan)
            feat_values.append(tmp_list)

        # Issue warning to user about missing data here if it exists.
        if len(missing_data) > 0:
            sys.stderr.write("WARNING: There are " + str(len(missing_data)) +
                             " elemental properties with missing values: \n")
            for key in missing_data:
                sys.stderr.write("\t"+key+":")
                for elem in missing_data[key]:
                    sys.stderr.write(" "+LookUpData.element_names[elem])
                sys.stderr.write("\n")

        features = pd.DataFrame(feat_values, columns=feat_headers)
        return features

    def add_elemental_property(self, property):
        """Function to add an elemental property to `self.elemental_properties`
        in order to be used to compute features.

        Parameters
        ----------
        property : str
            Property to be added.

        """

        if property not in self.elemental_properties:
            self.elemental_properties.append(property)

    def add_elemental_properties(self, properties):
        """Function to provide a list of elemental properties to be used to
        compute features.

        Parameters
        ----------
        properties : array-like
            Properties to be included. A list of strings containing property
            names.

        """

        for prop in properties:
            self.add_elemental_property(prop)

    def remove_elemental_property(self, property):
        """
        Function to remove an elemental property from
        `self.elemental_properties`.

        Parameters
        ----------
        property : str
            Property to be removed.

        """

        if property in self.elemental_properties:
            self.elemental_properties.remove(property)

    def remove_elemental_properties(self, properties):
        """Function to remove a list of elemental properties from the list of
        elemental properties.

        Parameters
        ----------
        properties : array-like
            Properties to be removed. A list of strings containing property
            names.

        """

        for prop in properties:
            self.remove_elemental_property(prop)
import sys
import types
import numpy as np
import pandas as pd
from ....data.materials.CompositionEntry import CompositionEntry
from ....data.materials.util.LookUpData import LookUpData

class ElementalPropertyAttributeGenerator:
    """Class to set up and generate descriptors based on elemental property
    statistics.

    Computes the mean, maximum, minimum, range, mode and mean
    absolute deviation of all elemental properties provided.

    Attributes
    ----------
    elemental_properties : array-like
        Elemental properties to be associated with this class for the
        generation of features.
    lookup-data : dict
        Dictionary containing the property name as the key and a list of
        floats as the value.

    """

    # Initialize to an empty list.
    elemental_properties = []

    def __init__(self, use_default_properties=True):
        """Class constructor.

        Parameters
        ----------
        use_default_properties : bool
            Flag to use default set of properties as defined below.

        """

        # Use default properties to reproduce Ward et al. descriptor values.
        if (use_default_properties):
            self.elemental_properties = ["Number", "MendeleevNumber",
                                         "AtomicWeight", "MeltingT",
                                         "Column", "Row", "CovalentRadius",
                                         "Electronegativity", "NsValence",
                                         "NpValence", "NdValence",
                                         "NfValence", "NValance",
                                         "NsUnfilled", "NpUnfilled",
                                         "NdUnfilled", "NfUnfilled",
                                         "NUnfilled", "GSvolume_pa",
                                         "GSbandgap", "GSmagmom",
                                         "SpaceGroupNumber"]

        # Initialize dictionary that will contain all the property values.
        self.lookup_data = {}

    def load_lookup_data(self):
        """Function to load the property values into `self.lookup_data` for the
        computation of features.

        """

        self.lookup_data = LookUpData.load_properties(
            self.elemental_properties)

    def generate_features(self, entries):
        """Function to generate features as mentioned in the class description.

        Parameters
        ----------
        entries : array-like
            Compositions for which features are to be generated. A list of
            CompositionEntry's.

        Returns
        ----------
        features : DataFrame
            Features for the given entries. Pandas data frame containing the
            names and values of the descriptors.

        Raises
        ------
        ValueError
            If no elemental properties are set.
            If input is not of type list.
            If items in the list are not CompositionEntry instances.

        """

        # Initialize lists of feature values and headers for pandas data frame.
        feat_values = []
        feat_headers = []

        # Make sure that there is at least one elemental property provided.
        if not self.elemental_properties:
            raise ValueError("No elemental property is set. Add at least one "
                             "property to compute meaningful descriptors.")

        # If the dictionary containing the property values is empty,
        # load values into it.
        if not self.lookup_data:
            self.load_lookup_data()

        # Raise exception if input argument is not of type list of
        # CompositionEntry's.
        if not isinstance(entries, list):
            raise ValueError("Argument should be of type list of "
                             "CompositionEntry's")
        elif (entries and not isinstance(entries[0], CompositionEntry)):
            raise ValueError("Argument should be of type list of "
                             "CompositionEntry's")

        # Insert header names here.
        n_statistics = 6
        for prop in self.elemental_properties:
            feat_headers.append("mean_"+prop)
            feat_headers.append("maxdiff_" + prop)
            feat_headers.append("dev_" + prop)
            feat_headers.append("max_" + prop)
            feat_headers.append("min_" + prop)
            feat_headers.append("most_" + prop)

        missing_data = {}
        # Generate features for each entry.
        for entry in entries:
            elem_ids = entry.get_element_ids()
            elem_fractions = entry.get_element_fractions()
            max_f = max(elem_fractions)
            tmp_list = []
            # Look up values for each property.
            for prop in self.elemental_properties:
                tmp_prop = []
                for elem_id in elem_ids:
                    tmp_prop_value = self.lookup_data[prop][elem_id]
                    # If data is missing, make a note of it so that we can
                    # inform the user later.
                    if np.isnan(tmp_prop_value):
                        if not prop in missing_data:
                            missing_data[prop] = []
                        if elem_id not in missing_data[prop]:
                            missing_data[prop].append(elem_id)
                    tmp_prop.append(tmp_prop_value)

                # If there is no missing data, compute statistics.
                if not np.isnan(tmp_prop).any():
                    mean_ = np.average(tmp_prop, weights=elem_fractions)
                    max_ = max(tmp_prop)
                    min_= min(tmp_prop)
                    max_diff_ = max_ - min_
                    avg_dev_ = np.average([abs(x - mean_) for x in tmp_prop],
                                          weights=elem_fractions)

                    indices = [i for i,f in enumerate(elem_fractions) if f >=
                               max_f]
                    most_ = sum(tmp_prop[i] for i in indices)/len(indices)

                    tmp_list.append(mean_)
                    tmp_list.append(max_diff_)
                    tmp_list.append(avg_dev_)
                    tmp_list.append(max_)
                    tmp_list.append(min_)
                    tmp_list.append(most_)
                else:
                    # Handle nan descriptors here from missing data.
                    for i in range(n_statistics):
                        tmp_list.append(np.nan)
            feat_values.append(tmp_list)

        # Issue warning to user about missing data here if it exists.
        if len(missing_data) > 0:
            sys.stderr.write("WARNING: There are " + str(len(missing_data)) +
                             " elemental properties with missing values: \n")
            for key in missing_data:
                sys.stderr.write("\t"+key+":")
                for elem in missing_data[key]:
                    sys.stderr.write(" "+LookUpData.element_names[elem])
                sys.stderr.write("\n")

        features = pd.DataFrame(feat_values, columns=feat_headers)
        return features

    def add_elemental_property(self, property):
        """Function to add an elemental property to `self.elemental_properties`
        in order to be used to compute features.

        Parameters
        ----------
        property : str
            Property to be added.

        """

        if property not in self.elemental_properties:
            self.elemental_properties.append(property)

    def add_elemental_properties(self, properties):
        """Function to provide a list of elemental properties to be used to
        compute features.

        Parameters
        ----------
        properties : array-like
            Properties to be included. A list of strings containing property
            names.

        """

        for prop in properties:
            self.add_elemental_property(prop)

    def remove_elemental_property(self, property):
        """
        Function to remove an elemental property from
        `self.elemental_properties`.

        Parameters
        ----------
        property : str
            Property to be removed.

        """

        if property in self.elemental_properties:
            self.elemental_properties.remove(property)

    def remove_elemental_properties(self, properties):
        """Function to remove a list of elemental properties from the list of
        elemental properties.

        Parameters
        ----------
        properties : array-like
            Properties to be removed. A list of strings containing property
            names.

        """

        for prop in properties:
            self.remove_elemental_property(prop)
Class to set up and generate descriptors based on elemental property     statistics.      Computes the mean, maximum, minimum, range, mode and mean     absolute deviation of all elemental properties provided.      Attributes     ----------     elemental_properties : array-like         Elemental properties to be associated with this class for the         generation of features.     lookup-data : dict         Dictionary containing the property name as the key and a list of         floats as the value. # Initialize to an empty list. Class constructor.          Parameters         ----------         use_default_properties : bool             Flag to use default set of properties as defined below. # Use default properties to reproduce Ward et al. descriptor values. # Initialize dictionary that will contain all the property values. Function to load the property values into `self.lookup_data` for the         computation of features. Function to generate features as mentioned in the class description.          Parameters         ----------         entries : array-like             Compositions for which features are to be generated. A list of             CompositionEntry's.          Returns         ----------         features : DataFrame             Features for the given entries. Pandas data frame containing the             names and values of the descriptors.          Raises         ------         ValueError             If no elemental properties are set.             If input is not of type list.             If items in the list are not CompositionEntry instances. # Initialize lists of feature values and headers for pandas data frame. # Make sure that there is at least one elemental property provided. # If the dictionary containing the property values is empty, # load values into it. # Raise exception if input argument is not of type list of # CompositionEntry's. # Insert header names here. # Generate features for each entry. # Look up values for each property. # If data is missing, make a note of it so that we can # inform the user later. # If there is no missing data, compute statistics. # Handle nan descriptors here from missing data. # Issue warning to user about missing data here if it exists. Function to add an elemental property to `self.elemental_properties`         in order to be used to compute features.          Parameters         ----------         property : str             Property to be added. Function to provide a list of elemental properties to be used to         compute features.          Parameters         ----------         properties : array-like             Properties to be included. A list of strings containing property             names. Function to remove an elemental property from         `self.elemental_properties`.          Parameters         ----------         property : str             Property to be removed. Function to remove a list of elemental properties from the list of         elemental properties.          Parameters         ----------         properties : array-like             Properties to be removed. A list of strings containing property             names.