Commit 671c687a authored by Ibrahim's avatar Ibrahim
Browse files

merge conflict in rl/ppo

parents 709ed8a4 4be4c2cd

commonml/helpers/types.py

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+25 −0
Changes for commonml/helpers/types.py: 25 added lines, 0 removed lines.
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"""

Type-checking, -conversion operations.

"""







import datetime

from typing import Any



import numpy as np







def is_datetype(d: Any) -> bool:

    """

    Check if an object represents a date.

    

    Arguments:

        d {Any} -- Any instance

    

    Returns:

        bool -- True if it represents a date or a time.

    """

    return isinstance(d, (datetime.datetime, datetime.date, datetime.time)) \

           or np.issubdtype(getattr(d, 'dtype', None), np.datetime64)

commonml/plotting/__init__.py

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Empty file added.

commonml/plotting/surfaces.py

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Changes for commonml/plotting/surfaces.py: 71 added lines, 0 removed lines.
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from typing import Dict



import numpy as np

import matplotlib.pyplot as plt

from mpl_toolkits.mplot3d import Axes3D



from utils.type_operations import is_datetype



from ..helpers import is_datetype





def plot_surface(x: np.ndarray, y: np.ndarray, z: np.ndarray, ax=None, fig_kwargs:Dict={}, **kwargs) \

    -> Axes3D:

    """

    Plot a 3D surface given x, y, z coordinates.



    Arguments:

        x {np.ndarray} -- A 1D or 2D array (indexed as [x, y]). Can be numeric or

            date/time-like.

        y {np.ndarray} -- A 1D or 2D array (indexed as [x, y]). Can be numeric or

            date/time-like.

        z {np.ndarray} -- A 2D array indexed as [x, y].



    Keyword Arguments:

        ax {Axes3D} -- The axes on which to plot surface. (default: {None})

        fig_kwargs {Dict} -- Dictionary of arguments for plt.figure() creation.

        **kwargs -- Passed to `ax.plot_surface()`



    Returns:

        Axes3D -- The axes on which the surface was plotted.

    """

    if ax is None:

        fig = plt.figure(**fig_kwargs)

        ax = fig.add_subplot(111, projection='3d')



    xtime, ytime = is_datetype(x[0]), is_datetype(y[0])

    xgrid, ygrid, zgrid = map(np.asarray, (x, y, z))



    xdim = x.shape[0]

    ydim = y.shape[0] if y.ndim == 1 else y.shape[1]



    if xgrid.ndim == 1:

        xlabels = xgrid

        xgrid = np.repeat(x[:, None], axis=1, repeats=ydim)

    else:

        xlabels = xgrid[:, 0]

    if ygrid.ndim == 1:

        ylabels = ygrid

        ygrid = np.repeat(y[None, :], axis=0, repeats=xdim)

    else:

        ylabels = ygrid[0, :]



    if xtime:

        xgrid = np.repeat(np.arange(xdim).reshape(-1, 1), axis=1, repeats=ydim)

    if ytime:

        ygrid = np.repeat(np.arange(ydim).reshape(1, -1), axis=0, repeats=xdim)



    ax.plot_surface(xgrid, ygrid, zgrid, **kwargs)



    if xtime:

        xticklocs = np.asarray(tuple(filter(lambda x: 0 <= x < xdim, \

                                            ax.get_xticks()))).astype(int)

        ax.set_xticks(xticklocs)

        ax.set_xticklabels(xlabels[xticklocs], rotation=20)

    if ytime:

        yticklocs = np.asarray(tuple(filter(lambda x: 0 <= x < xdim, \

                                            ax.get_yticks()))).astype(int)

        ax.set_yticks(yticklocs)

        ax.set_yticklabels(ylabels[yticklocs], rotation=20)



    return ax

helpers/__init__.py

+3 −0
Changes for helpers/__init__.py: 3 added lines, 0 removed lines.
Original line number Diff line number Diff line
@@ -10,3 +10,6 @@ from .env import ( 
    rewards,

    get_from_env

)

from .types import (

    is_datetype

)
 
 No newline at end of file

helpers/models.py

+3 −2
Changes for helpers/models.py: 3 added lines, 2 removed lines.
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from typing import Any, Union, Dict

from typing import Any, OrderedDict, Union, Dict

from copy import deepcopy



from torch.nn import Module
@@ -6,7 +6,8 @@ from sklearn.base import BaseEstimator 






def clone(model: Union[BaseEstimator, Module], attrs: Dict[str, Any]=None) -> Union[BaseEstimator, Module]:

def clone(model: Union[BaseEstimator, Module, OrderedDict, Dict],

          attrs: Dict[str, Any]=None) -> Union[BaseEstimator, Module, OrderedDict, Dict]:

    """

    Copy a scikit-learn or pytorch model.