116 lines
3.6 KiB
Cython
116 lines
3.6 KiB
Cython
# Author: Nelle Varoquaux, Andrew Tulloch, Antony Lee
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# Uses the pool adjacent violators algorithm (PAVA), with the
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# enhancement of searching for the longest decreasing subsequence to
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# pool at each step.
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import numpy as np
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from cython cimport floating
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def _inplace_contiguous_isotonic_regression(floating[::1] y, floating[::1] w):
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cdef:
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Py_ssize_t n = y.shape[0], i, k
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floating prev_y, sum_wy, sum_w
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Py_ssize_t[::1] target = np.arange(n, dtype=np.intp)
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# target describes a list of blocks. At any time, if [i..j] (inclusive) is
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# an active block, then target[i] := j and target[j] := i.
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# For "active" indices (block starts):
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# w[i] := sum{w_orig[j], j=[i..target[i]]}
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# y[i] := sum{y_orig[j]*w_orig[j], j=[i..target[i]]} / w[i]
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with nogil:
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i = 0
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while i < n:
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k = target[i] + 1
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if k == n:
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break
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if y[i] < y[k]:
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i = k
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continue
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sum_wy = w[i] * y[i]
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sum_w = w[i]
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while True:
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# We are within a decreasing subsequence.
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prev_y = y[k]
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sum_wy += w[k] * y[k]
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sum_w += w[k]
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k = target[k] + 1
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if k == n or prev_y < y[k]:
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# Non-singleton decreasing subsequence is finished,
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# update first entry.
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y[i] = sum_wy / sum_w
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w[i] = sum_w
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target[i] = k - 1
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target[k - 1] = i
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if i > 0:
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# Backtrack if we can. This makes the algorithm
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# single-pass and ensures O(n) complexity.
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i = target[i - 1]
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# Otherwise, restart from the same point.
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break
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# Reconstruct the solution.
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i = 0
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while i < n:
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k = target[i] + 1
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y[i + 1 : k] = y[i]
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i = k
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def _make_unique(const floating[::1] X,
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const floating[::1] y,
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const floating[::1] sample_weights):
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"""Average targets for duplicate X, drop duplicates.
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Aggregates duplicate X values into a single X value where
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the target y is a (sample_weighted) average of the individual
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targets.
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Assumes that X is ordered, so that all duplicates follow each other.
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"""
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unique_values = len(np.unique(X))
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if floating is float:
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dtype = np.float32
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else:
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dtype = np.float64
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cdef floating[::1] y_out = np.empty(unique_values, dtype=dtype)
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cdef floating[::1] x_out = np.empty_like(y_out)
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cdef floating[::1] weights_out = np.empty_like(y_out)
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cdef floating current_x = X[0]
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cdef floating current_y = 0
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cdef floating current_weight = 0
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cdef int i = 0
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cdef int j
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cdef floating x
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cdef int n_samples = len(X)
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cdef floating eps = np.finfo(dtype).resolution
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for j in range(n_samples):
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x = X[j]
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if x - current_x >= eps:
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# next unique value
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x_out[i] = current_x
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weights_out[i] = current_weight
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y_out[i] = current_y / current_weight
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i += 1
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current_x = x
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current_weight = sample_weights[j]
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current_y = y[j] * sample_weights[j]
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else:
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current_weight += sample_weights[j]
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current_y += y[j] * sample_weights[j]
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x_out[i] = current_x
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weights_out[i] = current_weight
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y_out[i] = current_y / current_weight
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return(
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np.asarray(x_out[:i+1]),
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np.asarray(y_out[:i+1]),
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np.asarray(weights_out[:i+1]),
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)
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