Project dependencies may have API risk issues

[PyDeps]

Hi, In ML-From-Scratch, inappropriate dependency versioning constraints can cause risks.

Below are the dependencies and version constraints that the project is using

matplotlib
numpy
sklearn
pandas
cvxopt
scipy
progressbar33
terminaltables
gym

The version constraint == will introduce the risk of dependency conflicts because the scope of dependencies is too strict.
The version constraint No Upper Bound and * will introduce the risk of the missing API Error because the latest version of the dependencies may remove some APIs.

After further analysis, in this project,
The version constraint of dependency numpy can be changed to >=1.8.0,<=1.23.0rc3.
The version constraint of dependency pandas can be changed to >=0.4.0,<=1.2.5.

The above modification suggestions can reduce the dependency conflicts as much as possible,
and introduce the latest version as much as possible without calling Error in the projects.

The invocation of the current project includes all the following methods.

The calling methods from the numpy

numpy.linalg.eigh
numpy.linalg.eig
numpy.linalg.svd
numpy.linalg.norm
numpy.linalg.det
numpy.linalg.inv
numpy.linalg.pinv

The calling methods from the pandas

pandas.read_csv

The calling methods from the all methods

mlfromscratch.supervised_learning.LassoRegression.predict
w.T.dot
self.layer_input.T.dot
progressbar.Percentage
mlfromscratch.reinforcement_learning.DeepQNetwork
self.W.reshape
numpy.concatenate
KNN
model_builder
matplotlib.pyplot.legend
GAN.train
layer.backward_pass
mlfromscratch.utils.polynomial_features.dot
mlfromscratch.utils.to_categorical.astype
numpy.linalg.pinv
NotImplementedError
self.U_opt.update
self._determine_frequent_itemsets
self._mutate
NaiveBayes.fit
mlfromscratch.unsupervised_learning.KMeans
mlfromscratch.supervised_learning.NaiveBayes.fit
numpy.argsort
self._transaction_contains_items
sample_predictions.astype
l1_l2_regularization
sets.append
self.output_shape
self._calculate_centroids
self._get_frequent_items
mlfromscratch.supervised_learning.XGBoostRegressionTree.fit
accum_grad.transpose.reshape
mlfromscratch.supervised_learning.PolynomialRidgeRegression.predict
y_pred.append
mlfromscratch.supervised_learning.BayesianRegression.predict
mlfromscratch.deep_learning.NeuralNetwork.add
NeuralNetwork.add
self.layers.append
self._insert_tree
numpy.random.shuffle
self.combined.train_on_batch
mlfromscratch.supervised_learning.BayesianRegression.fit
self.output_activation
ClassificationTree.fit
sklearn.datasets.load_digits
mlfromscratch.supervised_learning.LassoRegression
numpy.mean
cutoff.i.parent2.layers.w0.copy
self.train_on_batch
grad_wrt_state.T.dot
self._transform
mlfromscratch.supervised_learning.XGBoost.predict
mlfromscratch.supervised_learning.KNN.predict
self._converged
self.GradientBoostingClassifier.super.__init__
self.omega0.X_X.np.linalg.pinv.dot
reversed
gym.make
accum_grad.transpose.ravel
mlfromscratch.utils.calculate_covariance_matrix
mlfromscratch.unsupervised_learning.PCA
y.np.array.astype
self.hidden_activation.gradient
self.sigmoid
numpy.split
sigmoid.dot
logging.basicConfig
self.memory.pop
self._get_frequent_itemsets
X.diag_gradient.X.T.dot.dot.np.linalg.pinv.dot
tmp_y2.astype
mlfromscratch.deep_learning.layers.Reshape
Autoencoder.train
self._construct_tree
self.ElasticNet.super.predict
diff.any
max
self._sample
mlfromscratch.deep_learning.NeuralNetwork
isinstance
self.LassoRegression.super.predict
mean.sample.T.dot
self.predict_value
diag_gradient.X.T.dot.dot
self.activation_func
tmp_y1.astype
os.path.dirname
j.i.axs.axis
neighbor_labels.astype
numpy.expand_dims
X.T.dot.dot
dqn.model.summary
batch.sum
numpy.mean.append
self._init_random_centroids
self._calculate_likelihood
grad_func
mlfromscratch.utils.make_diagonal
self._build_tree
matplotlib.pyplot.get_cmap
self.activation_func.gradient
self.build_encoder
mlfromscratch.reinforcement_learning.DeepQNetwork.play
mlfromscratch.supervised_learning.decision_tree.RegressionTree
mlfromscratch.supervised_learning.ParticleSwarmOptimizedNN
SupportVectorMachine
posteriors.append
LogisticLoss
hidden_output.T.dot
self.omega0.dot
gen_mult_ser
grad_wrt_state.dot.dot
mlfromscratch.supervised_learning.SupportVectorMachine.predict
self.model.train_on_batch
math.ceil
V.dot
sigmoid
self._forward_pass
layer.forward_pass
numpy.array
numpy.random.randint
numpy.linalg.norm
self.LinearRegression.super.__init__
self._calculate_support
numpy.round
mlfromscratch.deep_learning.NeuralNetwork.test_on_batch
LDA
numpy.power
matplotlib.pyplot.figure.add_subplot
LDA.fit
self.test_on_batch
mlfromscratch.supervised_learning.Adaboost
self.PolynomialRidgeRegression.super.__init__
mlfromscratch.utils.make_diagonal.dot
determine_padding
layer.initialize
self.save_imgs
mlfromscratch.supervised_learning.LinearRegression.fit
mlfromscratch.utils.data_operation.accuracy_score
self._split
self.find_frequent_itemsets
min
model.evolve.test_on_batch
numpy.repeat
self.__call__
self.build_discriminator
print
numpy.linalg.inv
mlfromscratch.supervised_learning.RandomForest.fit
X.reshape.repeat
LDA.predict
numpy.linspace
mlfromscratch.unsupervised_learning.PAM.predict
l2_regularization
self._calculate_fitness.append
RandomForest
enumerate
mlfromscratch.utils.divide_on_feature
mlfromscratch.utils.batch_iterator
mlfromscratch.utils.data_manipulation.train_test_split
X.resp.sum
mlfromscratch.supervised_learning.NaiveBayes.predict
mlfromscratch.utils.train_test_split
self._pool_forward
math.floor
sklearn.datasets.make_classification
layer.parameters
self.LassoRegression.super.__init__
numpy.clip
f.read.split
Perceptron.predict
mlfromscratch.unsupervised_learning.Apriori
calculate_std_dev
self.env.render
log2
numpy.identity
Perceptron.fit
X.reshape.dot
mlfromscratch.supervised_learning.SupportVectorMachine.fit
self._expectation
self.build_generator
tree.predict
numpy.tile
filter_width.filter_height.channels.np.arange.np.repeat.reshape
self.LassoRegression.super.fit
mlfromscratch.unsupervised_learning.PCA.transform
numpy.atleast_2d
self.discriminator.summary
batch_errors.append
self._backward_pass
numpy.linalg.det
self.ElasticNet.super.fit
layer.set_input_shape
mlfromscratch.deep_learning.layers.Conv2D
self._vote
self.sigmoid.gradient
mlfromscratch.utils.calculate_entropy
numpy.sum
self.model.predict
mlfromscratch.unsupervised_learning.PAM
individual.test_on_batch
index_combinations
progressbar.Bar
col.mean
numpy.ones
numpy.arange
cvxopt.solvers.qp
NaiveBayes
self.letters.index
t.accum_grad.T.dot
mlfromscratch.unsupervised_learning.FPGrowth.find_frequent_itemsets
cutoff.i.parent1.layers.W.copy
mlfromscratch.supervised_learning.GradientBoostingRegressor.fit
matplotlib.pyplot.title
range
mlfromscratch.reinforcement_learning.DeepQNetwork.train
matplotlib.pyplot.figure
mlfromscratch.supervised_learning.ElasticNet.fit
GradientBoostingClassifier.fit
numpy.insert
mlfromscratch.supervised_learning.Adaboost.predict
os.path.join
self.hidden_activation
self.PolynomialRidgeRegression.super.predict
mlfromscratch.deep_learning.loss_functions.CrossEntropy
self._expand_cluster
mlfromscratch.supervised_learning.PolynomialRidgeRegression.fit
mlfromscratch.utils.k_fold_cross_validation_sets
matplotlib.pyplot.axis
self._calculate_scatter_matrices
X.dot
frequent.append
self._get_non_medoids
mlfromscratch.supervised_learning.BayesianRegression
self.w0_opt.update
mlfromscratch.deep_learning.layers.ZeroPadding2D
mlfromscratch.deep_learning.activation_functions.Sigmoid
X.std
DCGAN.train
sklearn.datasets.load_iris
numpy.exp
self.regularization.grad
mlfromscratch.unsupervised_learning.DBSCAN.predict
Perceptron
self.XGBoostRegressionTree.super.fit
x.strip.replace
sigmoid.sum
rules.append
matplotlib.pyplot.close
Adaboost.predict
col.var
KNN.predict
matplotlib.pyplot.ylabel
self._build_model
mlfromscratch.unsupervised_learning.RBM
self.cmap
numpy.amax
S.np.linalg.pinv.V.dot.dot
self._calculate_prior
float
mlfromscratch.supervised_learning.XGBoost.fit
mlfromscratch.supervised_learning.LDA.predict
matplotlib.pyplot.subplots
mlfromscratch.supervised_learning.KNN
numpy.random.random_sample
self.multivariate_gaussian
image_to_column
numpy.bincount
matplotlib.pyplot.suptitle
mlfromscratch.supervised_learning.ClassificationTree.fit
self.env.close
numpy.random.normal
column_to_image
sklearn.datasets.make_moons
items.sort
mu_n.T.dot
sample_predictions.astype.np.bincount.argmax
activation_function
get_im2col_indices
self.kernel
X1.mean
cutoff.i.parent2.layers.W.copy
mlfromscratch.deep_learning.optimizers.Adam
self.print_tree
X_test.reshape.reshape
mlfromscratch.supervised_learning.GradientBoostingClassifier.fit
y_pred.np.sign.flatten
mlfromscratch.supervised_learning.MultiClassLDA
XGBoost.fit
pow
shuffle_data
self.W_opt.update
numpy.empty
conditional_database.append
cols.transpose.reshape
self._closest_centroid
XGBoost.predict
mlfromscratch.supervised_learning.GradientBoostingRegressor.predict
self._inherit_weights
accum_grad.reshape.transpose
mlfromscratch.utils.data_operation.calculate_covariance_matrix
mlfromscratch.utils.accuracy_score
self.transform
numpy.ravel
numpy.argmax
X_train.reshape.reshape
self._generate_candidates
terminaltables.AsciiTable
numpy.random.uniform
mnist.data.reshape
self.LinearRegression.super.fit
X.T.X_X.np.linalg.pinv.dot.dot
split_func
self._leaf_value_calculation
mlfromscratch.deep_learning.layers.Activation
mlfromscratch.utils.calculate_variance
mlfromscratch.supervised_learning.LogisticRegression.predict
self.param.X.dot.self.sigmoid.np.round.astype
self.loss.hess
mlfromscratch.supervised_learning.LinearRegression
mlfromscratch.supervised_learning.GradientBoostingRegressor
numpy.pad.transpose
i.self.parameters.append
progressbar.ProgressBar
model.evolve.add
self.w_updt.any
j.i.axs.imshow
self.autoencoder.train_on_batch
X.reshape.reshape
x.strip
self.PolynomialRegression.super.__init__
numpy.percentile
mlfromscratch.supervised_learning.LogisticRegression
numpy.tile.reshape
numpy.log
self._calculate_cost
mlfromscratch.supervised_learning.MultiClassLDA.plot_in_2d
covar.np.linalg.pinv.mean.sample.T.dot.dot
mlfromscratch.deep_learning.NeuralNetwork.summary
mlfromscratch.reinforcement_learning.DeepQNetwork.set_model
numpy.divide
numpy.var
numpy.add.at
noise.self.generator.predict.reshape
self.autoencoder.summary
t.accum_grad.dot
numpy.random.seed
t.X.dot
mlfromscratch.utils.polynomial_features
mlfromscratch.utils.to_categorical
mlfromscratch.supervised_learning.RegressionTree.predict
math.sqrt
model.velocity.append
numpy.random.rand
MultilayerPerceptron.fit
mlfromscratch.supervised_learning.XGBoostRegressionTree
y_pred.y.self.loss.hess.sum
mlfromscratch.supervised_learning.RegressionTree.fit
self._create_clusters
mlfromscratch.unsupervised_learning.Apriori.find_frequent_itemsets
super
layer.output_shape
mlfromscratch.supervised_learning.XGBoost
X.T.X.diag_gradient.X.T.dot.dot.np.linalg.pinv.dot.dot
i.parent.layers.W.copy
self.loss_function.gradient
mlfromscratch.deep_learning.layers.BatchNormalization
self.trees.append
self.W_col.T.dot
numpy.roll
self._initialize_weights
self.V_opt.update
i.self.trees.predict
mlfromscratch.unsupervised_learning.Apriori.generate_rules
slice
self._get_likelihoods
activation.activation_functions
SW.np.linalg.inv.dot
numpy.sign
mlfromscratch.supervised_learning.ClassificationTree
self.discriminator.train_on_batch
math.log
str
self._memorize
mlfromscratch.utils.euclidean_distance
NeuralNetwork.predict
self._get_non_medoids.append
model.evolve.predict
numpy.atleast_2d.reshape
numpy.argmax.any
hasattr
SupportVectorMachine.predict
self.X_col.T.accum_grad.dot.reshape
numpy.zeros
scipy.stats.chi2.rvs
self._initialize_population
DCGAN
XGBoost
SupportVectorMachine.fit
y_pred.y.dot
mlfromscratch.supervised_learning.LinearRegression.predict
self.RegressionTree.super.fit
self._update_weights
mlfromscratch.supervised_learning.ClassificationTree.predict
mlfromscratch.supervised_learning.Adaboost.fit
X.mean
NaiveBayes.predict
mlfromscratch.utils.standardize
mlfromscratch.supervised_learning.LDA
self.beta_opt.update
numpy.repeat.reshape
numpy.full
format
batch.dot
self.autoencoder.predict
self._determine_prefixes
os.path.abspath
mlfromscratch.deep_learning.loss_functions.SquareLoss
DecisionStump
self.autoencoder.layers.extend
self.population.append
scipy.stats.multivariate_normal.rvs
list
f.read
self.loss.gradient.dot
sklearn.datasets.make_regression
self._classify
cluster.append
sklearn.datasets.make_blobs
numpy.multiply
model
self._closest_medoid
numpy.shape
self.bar
self.RidgeRegression.super.__init__
total_mean._mean.dot
self._pool_backward
mlfromscratch.supervised_learning.GradientBoostingClassifier.predict
self.output_activation.gradient
numpy.unique
tuple
accum_grad.reshape.reshape
self.parameters.append
numpy.outer
mlfromscratch.utils.get_random_subsets
Adaboost.fit
self.activation.gradient
numpy.zeros_like
len
self._gain
numpy.array_equal
X.diag_gradient.dot.dot
self._get_frequent_items.index
S.np.linalg.pinv.V.dot.dot.dot
zip
y.shape.X.shape.model_builder.summary
self.clusters.append
self._has_infrequent_itemsets
self._init_random_medoids.copy
t.self.states.dot
self.reconstruct
self.env.step
self.size.X.repeat.repeat
MultilayerPerceptron
self.responsibility.argmax
loss
mlfromscratch.unsupervised_learning.DBSCAN
sample.dot
numpy.atleast_1d
sklearn.datasets.fetch_mldata
X.mean.X.T.dot
mlfromscratch.utils.normalize.dot
self.fit
itertools.combinations_with_replacement
mlfromscratch.deep_learning.layers.RNN
self._init_random_gaussians
centroid_i.clusters.append
self.model_builder
set
self._draw_scaled_inv_chi_sq
l1_regularization
self.ElasticNet.super.__init__
self.clfs.append
self.PolynomialRegression.super.predict
fig.savefig
sum
Y.mean
mlfromscratch.supervised_learning.LDA.fit
accum_grad.reshape.dot
setuptools.setup
LogisticRegression.predict
mlfromscratch.supervised_learning.LassoRegression.fit
self.initialize_weights
self.memory.append
mlfromscratch.supervised_learning.decision_tree.RegressionTree.predict
X_X.np.linalg.pinv.dot
X_col.np.argmax.flatten
cmap
cutoff.i.parent1.layers.w0.copy
copy.copy
self._crossover
layer.layer_name
y_pred.y.self.loss.gradient.y.sum
name.activation_functions
self.hidden_activation.dot
self.GradientBoostingRegressor.super.__init__
numpy.pad.reshape
omega_n.dot
GradientBoostingClassifier.predict
numpy.bincount.argmax
mlfromscratch.supervised_learning.Perceptron.fit
numpy.inner
mlfromscratch.supervised_learning.GradientBoostingClassifier
join
matplotlib.pyplot.scatter
matplotlib.pyplot.show
self._rules_from_itemset
matplotlib.pyplot.xlabel
MultilayerPerceptron.predict
transaction.sort
mlfromscratch.supervised_learning.PolynomialRidgeRegression
mlfromscratch.supervised_learning.LogisticRegression.fit
numpy.diag
itertools.combinations
self._sample.dot
medoid_i.clusters.append
numpy.append
mlfromscratch.deep_learning.NeuralNetwork.predict
self.env.reset
X.T.dot
LogisticRegression
random.sample
self.generator.predict
self.responsibilities.append
mlfromscratch.utils.Plot.plot_in_2d
setuptools.find_packages
self._get_cluster_labels
Adaboost
mlfromscratch.supervised_learning.RegressionTree
self.PolynomialRegression.super.fit
self.log_func
mlfromscratch.unsupervised_learning.RBM.fit
Autoencoder
self.build_decoder
mlfromscratch.unsupervised_learning.GeneticAlgorithm.run
self.responsibility.sum
self.loss_function.acc
numpy.linalg.svd
mlfromscratch.deep_learning.layers.UpSampling2D
eigenvalues.argsort
mlfromscratch.supervised_learning.RandomForest
numpy.pad
mlfromscratch.unsupervised_learning.FPGrowth
mlfromscratch.supervised_learning.Neuroevolution
mlfromscratch.supervised_learning.RandomForest.predict
self._maximization
self._get_itemset_key
cols.transpose.transpose
pandas.read_csv
self._construct_training_set
self.training_reconstructions.append
numpy.linalg.eig
numpy.argmax.astype
math.exp
self.progressbar
table_data.append
mlfromscratch.deep_learning.layers.Dropout
numpy.expand_dims.dot
numpy.max
self._get_neighbors
NeuralNetwork.fit
mlfromscratch.supervised_learning.Perceptron
self.training_errors.append
RandomForest.predict
cvxopt.matrix
cov_tot.np.linalg.pinv.dot
matplotlib.pyplot.plot
X.T.X_sq_reg_inv.dot.dot
mlfromscratch.deep_learning.layers.Flatten
mlfromscratch.unsupervised_learning.GaussianMixtureModel
self.generator.summary
int
fig.add_subplot.scatter
numpy.where
self.GradientBoostingClassifier.super.fit
mlfromscratch.unsupervised_learning.GaussianMixtureModel.predict
FPTreeNode
neighbors.append
self.visited_samples.append
self.activation
self.regularization
cnt.gen_imgs.reshape
RandomForest.fit
mlfromscratch.supervised_learning.Perceptron.predict
DecisionNode
self._initialize
i.parent.layers.w0.copy
self.loss.gradient
mlfromscratch.supervised_learning.ElasticNet.predict
self.omega0.self.mu0.T.dot.dot
self.errors.append
self.freq_itemsets.append
main
abs
self._generate_candidates.append
self.combined.layers.extend
self.W_col.dot
i.self.trees.fit
mlfromscratch.supervised_learning.SupportVectorMachine
numpy.random.random
self.ClassificationTree.super.fit
GradientBoostingClassifier
x.startswith
GAN
items.append
batch_error.append
codecs.open
self.discriminator.set_trainable
ClassificationTree.predict
Rule
self.loss_function.loss
self._init_random_medoids
numpy.random.binomial
self._initialize_parameters
mlfromscratch.supervised_learning.XGBoostRegressionTree.predict
self.mu0.T.dot
self.layers.output_shape
mlfromscratch.utils.mean_squared_error
self._impurity_calculation
mean.X.T.dot
numpy.prod
model.evolve.evolve
LogisticRegression.fit
calculate_variance
y.T.dot
numpy.sqrt
mlfromscratch.deep_learning.layers.Dense
mlfromscratch.unsupervised_learning.KMeans.predict
numpy.vstack
class_distr.append
mlfromscratch.deep_learning.NeuralNetwork.fit
negative_visible.T.dot
mlfromscratch.utils.data_operation.mean_squared_error
numpy.expand_dims.sum
numpy.random.choice
self.gamma_opt.update
progressbar.ETA
mlfromscratch.utils.Plot
X.astype.astype
mlfromscratch.supervised_learning.NaiveBayes
NeuralNetwork
mlfromscratch.unsupervised_learning.GeneticAlgorithm
ClassificationTree
mlfromscratch.utils.normalize
self.PolynomialRidgeRegression.super.fit
batch.T.dot
self._is_prefix
mlfromscratch.deep_learning.activation_functions.Softmax
X2.mean
math.pow
subsets.append
imgs.self.autoencoder.predict.reshape
numpy.linalg.eigh
mlfromscratch.supervised_learning.ElasticNet
y.reshape
self._calculate_fitness
self._select_action

@developer
Could please help me check this issue?
May I pull a request to fix it?
Thank you very much.