Remove DeprecationWarning of np.int, np.float, np.bool, np.complex

.github/actions/install-aqua/action.yml

@@ -1,6 +1,6 @@
 # This code is part of Qiskit.
 #
-# (C) Copyright IBM 2020.
+# (C) Copyright IBM 2020, 2021.
 #
 # This code is licensed under the Apache License, Version 2.0. You may
 # obtain a copy of this license in the LICENSE.txt file in the root directory
@@ -20,7 +20,9 @@ runs:
         if [ -f "$HOME/miniconda/etc/profile.d/conda.sh" ]; then
           source "$HOME/miniconda/etc/profile.d/conda.sh"
           conda activate
+          pip install -e .[torch,cplex,cvx,pyscf]
+        else
+          pip install -e .[torch,cplex,cvx,pyscf,skquant]
         fi
-        pip install -e .[torch,cplex,cvx,pyscf,skquant]
         pip install -U -c constraints.txt -r requirements-dev.txt
       shell: bash

qiskit/aqua/algorithms/classifiers/qsvm/_qsvm_binary.py

@@ -1,6 +1,6 @@
 # This code is part of Qiskit.
 #
-# (C) Copyright IBM 2018, 2020.
+# (C) Copyright IBM 2018, 2021.
 #
 # This code is licensed under the Apache License, Version 2.0. You may
 # obtain a copy of this license in the LICENSE.txt file in the root directory
@@ -78,7 +78,7 @@ def train(self, data, labels):
         kernel_matrix = self._qalgo.construct_kernel_matrix(data)
         lambda2 = self._qalgo.lambda2
         labels = labels * 2 - 1  # map label from 0 --> -1 and 1 --> 1
-        labels = labels.astype(np.float)
+        labels = labels.astype(float)
         [alpha, b, support] = optimize_svm(kernel_matrix, labels, scaling=scaling, lambda2=lambda2)
         support_index = np.where(support)
         alphas = alpha[support_index]

qiskit/aqua/algorithms/classifiers/sklearn_svm/_sklearn_svm_binary.py

@@ -1,6 +1,6 @@
 # This code is part of Qiskit.
 #
-# (C) Copyright IBM 2018, 2020.
+# (C) Copyright IBM 2018, 2021.
 #
 # This code is licensed under the Apache License, Version 2.0. You may
 # obtain a copy of this license in the LICENSE.txt file in the root directory
@@ -43,7 +43,7 @@ def train(self, data, labels):
             data (dict): dictionary which maps each class to the points in the class
             labels (list): list of classes. For example: ['A', 'B']
         """
-        labels = labels.astype(np.float)
+        labels = labels.astype(float)
         labels = labels * 2. - 1.
         kernel_matrix = self.construct_kernel_matrix(data, data, self.gamma)
         self._ret['kernel_matrix_training'] = kernel_matrix
@@ -94,9 +94,9 @@ def test(self, data, labels):
 
             logger.debug("\n=============================================")
             logger.debug('classifying %s.', data[tin])
-            logger.debug('Label should be %s.', self.label_to_class[np.int(labels[tin])])
-            logger.debug('Predicted label is %s.', self.label_to_class[np.int(lsign[tin])])
-            if np.int(labels[tin]) == np.int(lsign[tin]):
+            logger.debug('Label should be %s.', self.label_to_class[int(labels[tin])])
+            logger.debug('Predicted label is %s.', self.label_to_class[int(lsign[tin])])
+            if int(labels[tin]) == int(lsign[tin]):
                 logger.debug('CORRECT')
             else:
                 logger.debug('INCORRECT')
@@ -131,7 +131,7 @@ def predict(self, data):
             for sin in range(len(svms)):
                 _l = yin[sin] * alphas[sin] * kernel_matrix[tin][sin]
                 ltot += _l
-            lsign[tin] = np.int((np.sign(ltot + bias) + 1.) / 2.)
+            lsign[tin] = int((np.sign(ltot + bias) + 1.) / 2.)
         self._ret['predicted_labels'] = lsign
         return lsign
 

qiskit/aqua/components/multiclass_extensions/all_pairs.py

@@ -1,6 +1,6 @@
 # This code is part of Qiskit.
 #
-# (C) Copyright IBM 2018, 2020.
+# (C) Copyright IBM 2018, 2021.
 #
 # This code is licensed under the Apache License, Version 2.0. You may
 # obtain a copy of this license in the LICENSE.txt file in the root directory
@@ -108,7 +108,7 @@ def predict(self, x):
                 estimator = estimators_from_i[j]
                 confidence = np.ravel(estimator.decision_function(x))
 
-                indices = (confidence > 0).astype(np.int)
+                indices = (confidence > 0).astype(int)
                 prediction = self.classes_[indices]
 
                 predictions.append(prediction.reshape(-1, 1))

qiskit/aqua/components/multiclass_extensions/error_correcting_code.py

@@ -1,6 +1,6 @@
 # This code is part of Qiskit.
 #
-# (C) Copyright IBM 2018, 2020.
+# (C) Copyright IBM 2018, 2021.
 #
 # This code is licensed under the Apache License, Version 2.0. You may
 # obtain a copy of this license in the LICENSE.txt file in the root directory
@@ -89,7 +89,7 @@ def train(self, x, y):
         self.codebook[self.codebook != 1] = 0
         classes_index = dict((c, i) for i, c in enumerate(self.classes))
         Y = np.array([self.codebook[classes_index[y[i]]]
-                      for i in range(x.shape[0])], dtype=np.int)
+                      for i in range(x.shape[0])], dtype=int)
         # pylint: disable=unsubscriptable-object
         logger.info("Require %s estimators.", Y.shape[1])
         for i in range(Y.shape[1]):

qiskit/aqua/operators/legacy/common.py

@@ -1,6 +1,6 @@
 # This code is part of Qiskit.
 #
-# (C) Copyright IBM 2019, 2020.
+# (C) Copyright IBM 2019, 2021.
 #
 # This code is licensed under the Apache License, Version 2.0. You may
 # obtain a copy of this license in the LICENSE.txt file in the root directory
@@ -72,7 +72,7 @@ def measure_pauli_z(data, pauli):
     num_shots = sum(data.values())
     p_z_or_x = np.logical_or(pauli.z, pauli.x)
     for key, value in data.items():
-        bitstr = np.asarray(list(key))[::-1].astype(np.int).astype(np.bool)
+        bitstr = np.asarray(list(key))[::-1].astype(int).astype(bool)
         # pylint: disable=no-member
         sign = -1.0 if np.logical_xor.reduce(np.logical_and(bitstr, p_z_or_x)) else 1.0
         observable += sign * value
@@ -105,7 +105,7 @@ def covariance(data, pauli_1, pauli_2, avg_1, avg_2):
     p1_z_or_x = np.logical_or(pauli_1.z, pauli_1.x)
     p2_z_or_x = np.logical_or(pauli_2.z, pauli_2.x)
     for key, value in data.items():
-        bitstr = np.asarray(list(key))[::-1].astype(np.int).astype(np.bool)
+        bitstr = np.asarray(list(key))[::-1].astype(int).astype(bool)
         # pylint: disable=no-member
         sign_1 = -1.0 if np.logical_xor.reduce(np.logical_and(bitstr, p1_z_or_x)) else 1.0
         sign_2 = -1.0 if np.logical_xor.reduce(np.logical_and(bitstr, p2_z_or_x)) else 1.0
@@ -255,7 +255,7 @@ def evolution_instruction(pauli_list, evo_time, num_time_slices,
         ValueError: Unrecognized pauli
     """
 
-    if not isinstance(power, (int, np.int)) or power < 1:
+    if not isinstance(power, int) or power < 1:
         raise AquaError("power must be an integer and greater or equal to 1.")
 
     state_registers = QuantumRegister(pauli_list[0][1].num_qubits)

qiskit/aqua/operators/legacy/weighted_pauli_operator.py

@@ -1,6 +1,6 @@
 # This code is part of Qiskit.
 #
-# (C) Copyright IBM 2019, 2020.
+# (C) Copyright IBM 2019, 2021.
 #
 # This code is licensed under the Apache License, Version 2.0. You may
 # obtain a copy of this license in the LICENSE.txt file in the root directory
@@ -262,7 +262,7 @@ def _scaling_weight(self, scaling_factor, copy=False):
         Raises:
             ValueError: the scaling factor is not a valid type.
         """
-        if not isinstance(scaling_factor, (int, float, complex, np.int, np.float, np.complex)):
+        if not isinstance(scaling_factor, (int, float, complex)):
             raise ValueError(
                 "Type of scaling factor is a valid type. {} if given.".format(
                     scaling_factor.__class__))
@@ -292,14 +292,14 @@ def multiply(self, other):
 
     def __rmul__(self, other):
         """ Overload other * self """
-        if isinstance(other, (int, float, complex, np.int, np.float, np.complex)):
+        if isinstance(other, (int, float, complex)):
             return self._scaling_weight(other, copy=True)
         else:
             return other.multiply(self)
 
     def __mul__(self, other):
         """ Overload self * other """
-        if isinstance(other, (int, float, complex, np.int, np.float, np.complex)):
+        if isinstance(other, (int, float, complex)):
             return self._scaling_weight(other, copy=True)
         else:
             return self.multiply(other)
@@ -1100,7 +1100,7 @@ def find_Z2_symmetries(cls, operator) -> 'Z2Symmetries':  # pylint: disable=inva
             return cls([], [], [], None)
 
         for pauli in operator.paulis:
-            stacked_paulis.append(np.concatenate((pauli[1].x, pauli[1].z), axis=0).astype(np.int))
+            stacked_paulis.append(np.concatenate((pauli[1].x, pauli[1].z), axis=0).astype(int))
 
         stacked_matrix = np.array(np.stack(stacked_paulis))
         symmetries = kernel_F2(stacked_matrix)

qiskit/aqua/operators/primitive_ops/pauli_op.py

@@ -1,6 +1,6 @@
 # This code is part of Qiskit.
 #
-# (C) Copyright IBM 2020.
+# (C) Copyright IBM 2020, 2021.
 #
 # This code is licensed under the Apache License, Version 2.0. You may
 # obtain a copy of this license in the LICENSE.txt file in the root directory
@@ -221,7 +221,7 @@ def eval(self,
                 corrected_z_bits = self.primitive.z[::-1]  # type: ignore
 
                 for bstr, v in front.primitive.items():
-                    bitstr = np.asarray(list(bstr)).astype(np.int).astype(np.bool)
+                    bitstr = np.asarray(list(bstr)).astype(int).astype(bool)
                     new_b_str = np.logical_xor(bitstr, corrected_x_bits)
                     new_str = ''.join(map(str, 1 * new_b_str))
                     z_factor = np.product(1 - 2 * np.logical_and(bitstr, corrected_z_bits))

qiskit/aqua/operators/primitive_ops/pauli_sum_op.py

@@ -1,6 +1,6 @@
 # This code is part of Qiskit.
 #
-# (C) Copyright IBM 2020.
+# (C) Copyright IBM 2020, 2021.
 #
 # This code is licensed under the Apache License, Version 2.0. You may
 # obtain a copy of this license in the LICENSE.txt file in the root directory
@@ -254,7 +254,7 @@ def eval(
                 coeffs = self.primitive.coeffs  # type:ignore
 
                 for bstr, v in front.primitive.items():
-                    bitstr = np.asarray(list(bstr)).astype(np.int).astype(np.bool)
+                    bitstr = np.asarray(list(bstr)).astype(int).astype(bool)
                     new_b_str = np.logical_xor(bitstr, corrected_x_bits)
                     new_str = ["".join(map(str, 1 * bs)) for bs in new_b_str]
                     z_factor = np.product(1 - 2 * np.logical_and(bitstr, corrected_z_bits), axis=1)

qiskit/aqua/utils/random_matrix_generator.py

@@ -1,6 +1,6 @@
 # This code is part of Qiskit.
 #
-# (C) Copyright IBM 2018, 2020.
+# (C) Copyright IBM 2018, 2021.
 #
 # This code is licensed under the Apache License, Version 2.0. You may
 # obtain a copy of this license in the LICENSE.txt file in the root directory
@@ -29,7 +29,7 @@ def random_h1_body(N):  # pylint: disable=invalid-name
         N (int): the number of spin orbitals.
 
     Returns:
-        np.ndarray: a 2-D matrix with np.complex data type.
+        np.ndarray: a 2-D matrix with complex data type.
 
     Raises:
         ValueError: invalid number of spin orbitals
@@ -53,7 +53,7 @@ def random_unitary(N):  # pylint: disable=invalid-name
         N (int): the dimension of unitary matrix
 
     Returns:
-        np.ndarray: a 2-D matrix with np.complex data type.
+        np.ndarray: a 2-D matrix with complex data type.
     """
     x = (aqua_globals.random.random(size=(N, N)) * N + 1j
          * aqua_globals.random.random(size=(N, N)) * N) / np.sqrt(2)
@@ -72,7 +72,7 @@ def random_h2_body(N, M):  # pylint: disable=invalid-name
         M (int) : number of non-zero entries
 
     Returns:
-        np.ndarray: a numpy 4-D tensor with np.complex data type.
+        np.ndarray: a numpy 4-D tensor with complex data type.
 
     Raises:
         ValueError: invalid spin orbitals

qiskit/chemistry/bksf.py

@@ -1,6 +1,6 @@
 # This code is part of Qiskit.
 #
-# (C) Copyright IBM 2018, 2020.
+# (C) Copyright IBM 2018, 2021.
 #
 # This code is licensed under the Apache License, Version 2.0. You may
 # obtain a copy of this license in the LICENSE.txt file in the root directory
@@ -155,7 +155,7 @@ def bravyi_kitaev_fast_edge_list(fer_op):
     h_1 = fer_op.h1
     h_2 = fer_op.h2
     modes = fer_op.modes
-    edge_matrix = np.zeros((modes, modes), dtype=np.bool)
+    edge_matrix = np.zeros((modes, modes), dtype=bool)
 
     for p, q in itertools.product(range(modes), repeat=2):  # pylint: disable=invalid-name
 

qiskit/chemistry/bosonic_operator.py

@@ -1,6 +1,6 @@
 # This code is part of Qiskit.
 #
-# (C) Copyright IBM 2020.
+# (C) Copyright IBM 2020, 2021.
 #
 # This code is licensed under the Apache License, Version 2.0. You may
 # obtain a copy of this license in the LICENSE.txt file in the root directory
@@ -71,11 +71,11 @@ def _direct_mapping(self, n: int) -> List[Tuple[Pauli, Pauli]]:
         paulis = []
 
         for i in range(n):
-            a_z = np.asarray([0] * i + [0] + [0] * (n - i - 1), dtype=np.bool)
-            a_x = np.asarray([0] * i + [1] + [0] * (n - i - 1), dtype=np.bool)
+            a_z = np.asarray([0] * i + [0] + [0] * (n - i - 1), dtype=bool)
+            a_x = np.asarray([0] * i + [1] + [0] * (n - i - 1), dtype=bool)
 
-            b_z = np.asarray([0] * i + [1] + [0] * (n - i - 1), dtype=np.bool)
-            b_x = np.asarray([0] * i + [1] + [0] * (n - i - 1), dtype=np.bool)
+            b_z = np.asarray([0] * i + [1] + [0] * (n - i - 1), dtype=bool)
+            b_x = np.asarray([0] * i + [1] + [0] * (n - i - 1), dtype=bool)
 
             paulis.append((Pauli(a_z, a_x), Pauli(b_z, b_x)))
 
@@ -141,16 +141,16 @@ def _combine(self, modes: List[int], paulis: dict, coeff: float) -> WeightedPaul
         if m in modes:
             pauli_list = paulis[m]
         else:
-            a_z = np.asarray([0] * self._basis[m], dtype=np.bool)
-            a_x = np.asarray([0] * self._basis[m], dtype=np.bool)
+            a_z = np.asarray([0] * self._basis[m], dtype=bool)
+            a_x = np.asarray([0] * self._basis[m], dtype=bool)
             pauli_list = [(1, Pauli(a_z, a_x))]
 
         for m in range(1, self._num_modes):
             if m in modes:
                 new_list = paulis[m]
             else:
-                a_z = np.asarray([0] * self._basis[m], dtype=np.bool)
-                a_x = np.asarray([0] * self._basis[m], dtype=np.bool)
+                a_z = np.asarray([0] * self._basis[m], dtype=bool)
+                a_x = np.asarray([0] * self._basis[m], dtype=bool)
                 new_list = [(1, Pauli(a_z, a_x))]
             pauli_list = self._extend(pauli_list, new_list)
 

qiskit/chemistry/circuit/library/initial_states/hartree_fock.py

@@ -1,6 +1,6 @@
 # This code is part of Qiskit.
 #
-# (C) Copyright IBM 2018, 2020.
+# (C) Copyright IBM 2018, 2021.
 #
 # This code is licensed under the Apache License, Version 2.0. You may
 # obtain a copy of this license in the LICENSE.txt file in the root directory
@@ -99,15 +99,15 @@ def hartree_fock_bitstring(num_orbitals: int,
         raise ValueError('# of particles must be less than or equal to # of orbitals.')
 
     half_orbitals = num_orbitals // 2
-    bitstr = np.zeros(num_orbitals, np.bool)
+    bitstr = np.zeros(num_orbitals, bool)
     bitstr[-num_alpha:] = True
     bitstr[-(half_orbitals + num_beta):-half_orbitals] = True
 
     if qubit_mapping == 'parity':
         new_bitstr = bitstr.copy()
 
         t_r = np.triu(np.ones((num_orbitals, num_orbitals)))
-        new_bitstr = t_r.dot(new_bitstr.astype(np.int)) % 2  # pylint: disable=no-member
+        new_bitstr = t_r.dot(new_bitstr.astype(int)) % 2  # pylint: disable=no-member
 
         bitstr = np.append(new_bitstr[1:half_orbitals], new_bitstr[half_orbitals + 1:]) \
             if two_qubit_reduction else new_bitstr
@@ -122,10 +122,10 @@ def hartree_fock_bitstring(num_orbitals: int,
         start_idx = beta.shape[0] - num_orbitals
         beta = beta[start_idx:, start_idx:]
         new_bitstr = beta.dot(bitstr.astype(int)) % 2
-        bitstr = new_bitstr.astype(np.bool)
+        bitstr = new_bitstr.astype(bool)
 
     if sq_list is not None:
         sq_list = [len(bitstr) - 1 - position for position in sq_list]
         bitstr = np.delete(bitstr, sq_list)
 
-    return bitstr.astype(np.bool)
+    return bitstr.astype(bool)

qiskit/chemistry/circuit/library/initial_states/vscf.py

@@ -1,6 +1,6 @@
 # This code is part of Qiskit.
 #
-# (C) Copyright IBM 2020.
+# (C) Copyright IBM 2020, 2021.
 #
 # This code is licensed under the Apache License, Version 2.0. You may
 # obtain a copy of this license in the LICENSE.txt file in the root directory
@@ -60,7 +60,7 @@ def vscf_bitstring(basis: List[int]) -> np.ndarray:
         The bitstring representing the state of the VSCF state as array of bools.
     """
     num_qubits = sum(basis)
-    bitstr = np.zeros(num_qubits, np.bool)
+    bitstr = np.zeros(num_qubits, bool)
     count = 0
     for modal in basis:
         bitstr[num_qubits - count - 1] = True

qiskit/chemistry/components/variational_forms/uccsd.py

@@ -1,6 +1,6 @@
 # This code is part of Qiskit.
 #
-# (C) Copyright IBM 2018, 2020.
+# (C) Copyright IBM 2018, 2021.
 #
 # This code is licensed under the Apache License, Version 2.0. You may
 # obtain a copy of this license in the LICENSE.txt file in the root directory
@@ -452,7 +452,7 @@ def preferred_init_points(self):
         if self._initial_state is None:
             return None
         else:
-            return np.zeros(self._num_parameters, dtype=np.float)
+            return np.zeros(self._num_parameters, dtype=float)
 
     @staticmethod
     def compute_excitation_lists(num_particles, num_orbitals, active_occ_list=None,