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"""A small personal package created to store code and data I often reuse. |
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I'll continue to update it with useful functions that I find myself reusing. The `apoor.data` module has some common datasets and functions for reading them in as pandas DataFrames. |
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""" |
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# Version string |
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__version__ = "1.1.9" |
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import itertools as it |
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import numpy as np |
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from . import data |
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from typing import Any, List, Callable, Tuple |
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def fdir(o: Any) -> List[str]: |
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"""Filtered dir(). Same as builtin dir() |
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function without private attributes. |
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:param o: Object being inspected |
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:return: "Public attributes" of o |
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""" |
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return [a for a in dir(o) if a[0] != "_"] |
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def set_seed(n:int): |
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"""Sets numpy's random seed. |
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:param n: The value used to set numpy's random seed. |
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:type n: int |
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""" |
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np.random.seed(n) |
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def make_scale(dmin:float,dmax:float,rmin:float,rmax:float,clamp:bool=False) -> Callable[[float],float]: |
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"""Scale function factory. |
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Creates a scale function to map a number from a domain to a range. |
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:param dmin: Domain's start value |
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:param dmax: Domain's end value |
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:param rmin: Range's start value |
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:param rmax: Range's end value |
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:param clamp: If the result is outside the range, return |
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clamped value (default: False) |
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:return: A scale function taking one numeric argument and |
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returns the value mapped from the domain to the range |
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(and clamped if `clamp` flag is set). |
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Examples: |
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>>> s = make_scale(0,1,0,10) |
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>>> s(0.1) |
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1.0 |
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>>> s = make_scale(0,10,10,0) |
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>>> s(1.0) |
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9.0 |
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>>> s = make_scale(0,1,0,1,clamp=True) |
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>>> s(100) |
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1.0 |
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""" |
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drange = dmax - dmin |
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rrange = rmax - rmin |
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scale_factor = rrange / drange |
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def scale(n): |
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n_ = (n - dmin) * scale_factor + rmin |
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if clamp: return min(max(n_,rmin),rmax) |
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else: return n_ |
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return scale |
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def train_test_split(*arrays, test_pct: float = 0.15, val_set: bool = False, val_pct: float = 0.15) -> Tuple[np.ndarray]: |
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"""Splits arrays into train & test sets. |
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Splits arrays into train, test, and (optionally) validation sets using the supplied percentages. |
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:param *arrays: An arbitrary number of sequences to be split |
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into train, test, and (optionally) validation sets. Must |
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have at least one array. |
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:param test_pct: Float in the range ``[0,1]``. Percent of total |
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``n`` values to include in test set. |
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The train set will have `1.0 - test_pct` pct of |
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values (or `1.0 - test_pct - val_pct` pct of values |
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if `val_set == True`). |
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:param val_set: Whether or not to return a validation set, |
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in addition to a test set. |
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:param val_pct: `float` in the range ``[0,1]``. Percent |
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of total n values to include in test set. |
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Ignored if ``val_set == False``. |
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The train set will have ``1.0 - test_pct - val_pct`` |
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pct of values. |
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:returns: splits tuple of numpy arrays. Input arrays |
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split into train, test, val sets. |
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If ``val_set == False``, ``len(splits) == 2 * len(arrays)``, |
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or if ``val_set == True``, ``len(splits) == 3 * len(arrays)``. |
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Example: |
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>>> x = np.arange(10) |
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>>> train_test_split(x) |
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(array([3, 9, 4, 2, 1, 0, 7, 5, 8]), array([6])) |
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>>> x = np.arange(10) |
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>>> y = x[::-1] |
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>>> x_train, x_test, y_train, y_test = train_test_split(x,y) |
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>>> x_train, x_test, y_train, y_test |
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(array([1, 3, 5, 8, 4, 7, 6, 9]), |
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array([0, 2]), |
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array([8, 6, 4, 1, 5, 2, 3, 0]), |
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array([9, 7])) |
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>>> train_test_split(x,test_pct=0.3,val_set=True,val_pct=0.2) |
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(array([0, 9, 5, 7, 6, 2, 8]), |
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array([1, 3, 4]), |
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array([3, 4])) |
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""" |
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# Perform input checks |
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assert arrays, "No arrays supplied" |
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lens = [len(a) for a in arrays] |
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assert len(set(lens)) == 1, "arrays have varying lengths" |
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assert lens[0] > 0, "supplied arrays have `len == 0`" |
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if val_set: |
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assert 0.0 <= test_pct <= 1.0, "`test_pct` must be in the range `0.0 <= test_pct <= 1.0`" |
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assert 0.0 <= val_pct <= 1.0, "`val_pct` must be in the range `0.0 <= val_pct <= 1.0`" |
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assert test_pct + val_pct <= 1.0, "Can't have `test_pc + val_pct >= 1.0`" |
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else: |
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assert 0.0 <= test_pct <= 1.0, "`test_pct` must be in the range `0.0 <= test_pct <= 1.0`" |
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assert test_pct <= 1.0, "Can't have `test_pc >= 1.0`" |
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# Calculate lengths |
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n = lens[0] |
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n_test = int(n * test_pct) |
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# Shuffle the indexes |
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indexes = np.arange(n) |
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np.random.shuffle(indexes) |
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# Split the data |
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if val_set: |
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n_val = int(n * val_pct) |
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n_train = n - n_test - n_val |
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splits = ( |
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( |
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a[indexes[:n_train]], |
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a[indexes[n_train:n_train+n_test]], |
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a[indexes[-n_val:]] |
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) |
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for a in map(np.asarray,arrays) |
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) |
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else: |
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n_train = n - n_test |
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splits = ( |
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(a[indexes[:n_train]], a[indexes[n_train:]]) |
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for a in map(np.asarray,arrays) |
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) |
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return tuple(it.chain(*splits)) |
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def to_onehot(y: np.ndarray, num_classes: int = None, dtype="float32") -> np.ndarray: |
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"""Expands a 1D categorical vector to |
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a 2D, onehot-encoded categorical matrix. |
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:param y: 1D categorical vector |
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:param num_classes: Number of categories in (and width of) |
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the output matrix. If ``num_classes`` is ``None`, sets |
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to ``max(y) + 1``. |
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:param dtype: Data type of output matrix |
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:returns: 2D one-hot encoded matrix |
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Examples: |
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>>> data = np.array([0,2,1,3]) |
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>>> apoor.to_onehot(data) |
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array([[1., 0., 0., 0.], |
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[0., 0., 1., 0.], |
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[0., 1., 0., 0.], |
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[0., 0., 0., 1.]]) |
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""" |
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if num_classes is None: |
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num_classes = np.max(y) + 1 |
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return np.identity(num_classes,dtype=dtype)[y] |
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a-poor /
apoor
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