vene

vene

from sklearn.utils.metaestimators import if_delegate_has_method
from sklearn.utils.fixes import signature


class Test(object):
    def hi(self, what):
        return 1 + what


class Kid(object):

vene

import sys
import numpy as np
import sklearn
import lightning

print("python", sys.version)
print("numpy", np.__version__)
print("scikit-learn", sklearn.__version__)
print("lightning", lightning.__version__)

vene

import numpy as np
from sklearn.metrics import precision_recall_curve, average_precision_score


def naive_interpolated_precision(y_true, y_scores):
    precisions, recalls, _ = precision_recall_curve(y_true, y_scores)
    interp_precisions = []

    # the final point
    precisions = precisions[:-1]

vene

import numpy as np

from sklearn.datasets import load_iris
from sklearn.linear_model import LogisticRegression
from sklearn.utils import shuffle

# get shuffled iris data
X, y = load_iris(return_X_y=True)
X, y = shuffle(X, y, random_state=0)

vene

DYNET_PATH ?= /home/vlad/code/dynet
EIGEN_PATH ?= /home/vlad/code/eigen

CC = g++
DEBUG = -g
INCLUDES = -I$(DYNET_PATH) -I$(EIGEN_PATH) 
LIBS = -L$(DYNET_PATH)/build-cuda/dynet/ 
CFLAGS = -O3 -Wall -Wno-sign-compare -Wno-int-in-bool-context -c -fmessage-length=0 $(INCLUDES) -DEIGEN_FAST_MATH -fPIC -fno-finite-math-only -Wno-missing-braces -std=c++11 -funroll-loops
LFLAGS = $(LIBS) -ldynet

vene

# Author: Vlad Niculae <vlad@vene.ro>
# License: Simplified BSD

import numpy as np

try:
    from numba import jit
except ImportError:
    print("numba not available")
    def jit(nopython):

vene

# Author: vlad niculae <vlad@vene.ro>
# License: 3-clause BSD

import numpy as np
import cvxpy as cx

from copt.constraint import SimplexConstraint
from copt.splitting import minimize_primal_dual

vene

#pragma once

/* MLFlow interaction */

#include <string>
#include <chrono>

#include <cpr/cpr.h>
#include <nlohmann/json.hpp>

vene

import numpy as np


def numeric_grad(f, x, eps=1e-3):

    grad = np.zeros_like(x)

    for i in range(x.shape[0]):
        v = np.zeros_like(x)
        v[i] = 1