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Exponentiation | 2^2 # note the difference to python 2 ** 2 | _____no_output_____ | Apache-2.0 | _notebooks/2020-10-20-julia.ipynb | ChristopherTh/statistics-blog |
Remainder | 4 % 3 | _____no_output_____ | Apache-2.0 | _notebooks/2020-10-20-julia.ipynb | ChristopherTh/statistics-blog |
Negation | !true # note the difference to numpys ~ | _____no_output_____ | Apache-2.0 | _notebooks/2020-10-20-julia.ipynb | ChristopherTh/statistics-blog |
Equality | true == true | _____no_output_____ | Apache-2.0 | _notebooks/2020-10-20-julia.ipynb | ChristopherTh/statistics-blog |
Inequality | true != true | _____no_output_____ | Apache-2.0 | _notebooks/2020-10-20-julia.ipynb | ChristopherTh/statistics-blog |
Elementwise operation | [1 2; 3 3] .* [9 9;9 9] # elementwise
[1 2; 3 3] * [9 9;9 9] #matrix materix product | _____no_output_____ | Apache-2.0 | _notebooks/2020-10-20-julia.ipynb | ChristopherTh/statistics-blog |
Check for nan | isnan(9) | _____no_output_____ | Apache-2.0 | _notebooks/2020-10-20-julia.ipynb | ChristopherTh/statistics-blog |
Ternary operator The syntax iscond ? do_true : else | 1 != 1 ? println(3) : println(999) | 999
| Apache-2.0 | _notebooks/2020-10-20-julia.ipynb | ChristopherTh/statistics-blog |
And/or | true && true
false || true
| _____no_output_____ | Apache-2.0 | _notebooks/2020-10-20-julia.ipynb | ChristopherTh/statistics-blog |
Sources - [ ] https://juliadocs.github.io/Julia-Cheat-Sheet/- [ ] https://github.com/JuliaLang/julia- [ ] https://arxiv.org/pdf/2003.10146.pdf- [ ] https://github.com/h-Klok/StatsWithJuliaBook- [ ] juliahub- [ ] juliaacademy- [ ] https://www.sas.upenn.edu/~jesusfv/Chapter_HPC_8_Julia.pdf- [ ] https://www.packtpub.com/product/hands-on-design-patterns-and-best-practices-with-julia/9781838648817- [ ] https://www.elsevier.com/books/introduction-to-quantitative-macroeconomics-using-julia/caraiani/978-0-12-812219-8- [ ] https://colab.research.google.com/github/ageron/julia_notebooks/blob/master/Julia_for_Pythonistas.ipynbscrollTo=EEzvvzCl1i0F- [ ] https://cheatsheets.quantecon.org/ | _____no_output_____ | Apache-2.0 | _notebooks/2020-10-20-julia.ipynb | ChristopherTh/statistics-blog |
|
Eliminamos descargas anteriores | !rm *pdf | rm: *pdf: No such file or directory
| MIT | herramientas/procesamiento_informes_EPI/COVID_Descarga_y_Preprocesamiento_Informes_EPI_MINSAL.ipynb | DiazSalinas/COVID-19 |
Obtenemos las urls de los informes (EPI y Generales) | response = subprocess.check_output(shlex.split('curl --request GET https://www.gob.cl/coronavirus/cifrasoficiales/'))
url_reporte = []
url_informe_epi = []
for line in response.decode().splitlines():
if "Reporte_Covid19.pdf" in line:
url = line.strip().split('https://')[1].split("\"")[0]
url_reporte.append(url)
#El informe a veces está en minúsculas
elif "INFORME_EPI" in line:
test = line.strip()
test = test.split('https://')[1].split("\"")[0]
url_informe_epi.append(test)
url_informe_epi | _____no_output_____ | MIT | herramientas/procesamiento_informes_EPI/COVID_Descarga_y_Preprocesamiento_Informes_EPI_MINSAL.ipynb | DiazSalinas/COVID-19 |
Double Check | url_reporte
url_informe_epi | _____no_output_____ | MIT | herramientas/procesamiento_informes_EPI/COVID_Descarga_y_Preprocesamiento_Informes_EPI_MINSAL.ipynb | DiazSalinas/COVID-19 |
Descarga Informes | #for url in set(url_reporte):
# subprocess.check_output(shlex.split("wget "+ url))
for url in set(url_informe_epi):
subprocess.check_output(shlex.split("wget "+ url))
!ls | _____no_output_____ | MIT | herramientas/procesamiento_informes_EPI/COVID_Descarga_y_Preprocesamiento_Informes_EPI_MINSAL.ipynb | DiazSalinas/COVID-19 |
PreprocesamientoUsamos tabula-py: wrapper de Tabula App (escrita en Java). A library for extracting tables from PDF files https://github.com/chezou/tabula-py | import tabula
dfs_files = {}
for url in url_informe_epi:
pdf_file = url.split('/')[-1]
df = tabula.read_pdf(pdf_file, pages='all', multiple_tables=True)
fecha = pdf_file.split('_')[-1].split('.')[0]
print(fecha)
dfs_files['tablas_' + fecha] = df
| _____no_output_____ | MIT | herramientas/procesamiento_informes_EPI/COVID_Descarga_y_Preprocesamiento_Informes_EPI_MINSAL.ipynb | DiazSalinas/COVID-19 |
Verificamos algunas tablas | tablas_20200401 = dfs_files['tablas_20200401v2']
tablas_20200330 = dfs_files['tablas_20200330']
df_comunas_20200401 = {}
unnamed_primeraCol = {}
for idx, df in enumerate(tablas_20200401):
if 'Comuna' in df.columns:
key= 'tabla_' + str(idx + 1)
print(key)
df_comunas_20200401[key] = df
df_comunas_20200401['tabla_6'].head() | _____no_output_____ | MIT | herramientas/procesamiento_informes_EPI/COVID_Descarga_y_Preprocesamiento_Informes_EPI_MINSAL.ipynb | DiazSalinas/COVID-19 |
Tabla empieza con un *Unnamed: 0* | df_comunas_20200401['tabla_22'].head() | _____no_output_____ | MIT | herramientas/procesamiento_informes_EPI/COVID_Descarga_y_Preprocesamiento_Informes_EPI_MINSAL.ipynb | DiazSalinas/COVID-19 |
Tabla **no** empieza con un *Unnamed: 0* | df_comunas_20200330 = {}
unnamed_primeraCol = {}
for idx, df in enumerate(tablas_20200330):
if 'Comuna' in df.columns:
key= 'tabla_' + str(idx + 1)
print(key)
df_comunas_20200330[key] = df
df_comunas_20200330['tabla_7'].head() | _____no_output_____ | MIT | herramientas/procesamiento_informes_EPI/COVID_Descarga_y_Preprocesamiento_Informes_EPI_MINSAL.ipynb | DiazSalinas/COVID-19 |
Misma tabla empieza con un *Unnamed: 0* | df_comunas_20200330['tabla_23'].head() | _____no_output_____ | MIT | herramientas/procesamiento_informes_EPI/COVID_Descarga_y_Preprocesamiento_Informes_EPI_MINSAL.ipynb | DiazSalinas/COVID-19 |
Misma tabla **no** empieza con un *Unnamed: 0* Separamos estas dos categorias: | df_comunas_20200401 = {}
unnamed_primeraCol_20200401 = {}
for idx, df in enumerate(tablas_20200401):
if 'Comuna' in df.columns:
key = 'tabla_' + str(idx + 1)
df_comunas_20200401[key] = df
if 'Unnamed' in df.columns[0]:
print(key)
unnamed_primeraCol_20200401[key] = df
df_comunas_20200330 = {}
unnamed_primeraCol_20200330 = {}
for idx, df in enumerate(tablas_20200330):
if 'Comuna' in df.columns:
key= 'tabla_' + str(idx + 1)
df_comunas_20200330[key] = df
if 'Unnamed' in df.columns[0]:
print(key)
unnamed_primeraCol_20200330[key] = df | tabla_7
tabla_13
tabla_18
tabla_19
tabla_22
| MIT | herramientas/procesamiento_informes_EPI/COVID_Descarga_y_Preprocesamiento_Informes_EPI_MINSAL.ipynb | DiazSalinas/COVID-19 |
Resumen * El informe 20200330 tiene una tabla más al parecer (en realidad esto no es así y parecer que un cambio en el gráfico dejo la kgá).* La extracción de tablas parece tener los mismos errores en las mismas tablas. | %%capture
"""
for tup_1, tup_2 in zip(df_comunas.items(), df_comunas_2.items()):
key_1, df_1 = tup_1
key_2, df_2 = tup_2
if (key_1 or key_2) in unnamed_primeraCol:
if (df_1.columns == df_2.columns).all:
print("LAS COLUMNAS DE LAS TABLAS *diferentes* coinciden!", key_1, key_2)
""" | _____no_output_____ | MIT | herramientas/procesamiento_informes_EPI/COVID_Descarga_y_Preprocesamiento_Informes_EPI_MINSAL.ipynb | DiazSalinas/COVID-19 |
Estandarizamos las tablas | for key in df_comunas_20200401.keys():
df = df_comunas_20200401[key]
if key in unnamed_primeraCol_20200401.keys():
df['Comuna'] = df['Unnamed: 0']
df['N°'] = df['Unnamed: 1']
df['Tasa'] = df['Unnamed: 2']
df_comunas_20200401[key] = df.drop(labels='Unnamed: 0',
axis=1).drop(labels='Unnamed: 1',
axis=1).drop(labels='Unnamed: 2', axis=1)
else:
if key == 'tabla_22': continue
df_comunas_20200401[key] = df.drop(labels='Unnamed: 0',
axis=1).drop(labels='Unnamed: 1', axis=1)
for key in df_comunas_20200330.keys():
df = df_comunas_20200330[key]
if key in unnamed_primeraCol_20200330.keys():
df['Comuna'] = df['Unnamed: 0']
df['N°'] = df['Unnamed: 1']
df['Tasa'] = df['Unnamed: 2']
df_comunas_20200330[key] = df.drop(labels='Unnamed: 0',
axis=1).drop(labels='Unnamed: 1',
axis=1).drop(labels='Unnamed: 2', axis=1)
else:
if key == 'tabla_22': continue
df_comunas_20200330[key] = df.drop(labels='Unnamed: 0',
axis=1).drop(labels='Unnamed: 1',axis=1)
for key, region in df_comunas_20200401.items():
print(key, region.columns)
for key, region in df_comunas_20200330.items():
print(key, region.columns) | tabla_7 Index(['Comuna', 'N°', 'Población', 'Tasa'], dtype='object')
tabla_8 Index(['Comuna', 'N°', 'Población', 'Tasa'], dtype='object')
tabla_9 Index(['Comuna', 'N°', 'Población', 'Tasa'], dtype='object')
tabla_10 Index(['Comuna', 'N°', 'Población', 'Tasa'], dtype='object')
tabla_11 Index(['Comuna', 'N°', 'Población', 'Tasa'], dtype='object')
tabla_12 Index(['Comuna', 'N°', 'Población', 'Tasa'], dtype='object')
tabla_13 Index(['Comuna', 'N°', 'Población', 'Tasa'], dtype='object')
tabla_15 Index(['Comuna', 'N°', 'Población', 'Tasa'], dtype='object')
tabla_16 Index(['Comuna', 'N°', 'Población', 'Tasa'], dtype='object')
tabla_17 Index(['Comuna', 'N°', 'Población', 'Tasa'], dtype='object')
tabla_18 Index(['Comuna', 'N°', 'Población', 'Tasa'], dtype='object')
tabla_19 Index(['Comuna', 'N°', 'Población', 'Tasa'], dtype='object')
tabla_20 Index(['Comuna', 'N°', 'Población', 'Tasa'], dtype='object')
tabla_21 Index(['Comuna', 'N°', 'Población', 'Tasa'], dtype='object')
tabla_22 Index(['Comuna', 'N°', 'Población', 'Tasa'], dtype='object')
tabla_23 Index(['Comuna', 'N°', 'Población', 'Tasa', 'Unnamed: 2'], dtype='object')
| MIT | herramientas/procesamiento_informes_EPI/COVID_Descarga_y_Preprocesamiento_Informes_EPI_MINSAL.ipynb | DiazSalinas/COVID-19 |
Ultima tabla tiene *Unnamed: 2* | df_comunas_20200401['tabla_21']
df_comunas_20200330['tabla_23'] | _____no_output_____ | MIT | herramientas/procesamiento_informes_EPI/COVID_Descarga_y_Preprocesamiento_Informes_EPI_MINSAL.ipynb | DiazSalinas/COVID-19 |
Class with Multiple Objects | class Birds:
def __init__(self,bird_name):
self.bird_name = bird_name
def flying_birds(self):
print(f"{self.bird_name} flies above clouds")
def non_flying_birds(self):
print(f"{self.bird_name} is the national bird of the Philippines")
vulture = Birds("Griffon Vulture")
crane = Birds("Common Crane")
emu = Birds ("Emu")
vulture.flying_birds()
crane.flying_birds()
emu.non_flying_birds() | Griffon Vulture flies above clouds
Common Crane flies above clouds
Emu is the national bird of the Philippines
| Apache-2.0 | OOP_58001_OOP_Concepts_2.ipynb | AndreiBenavidez/OOP-58001 |
Encapsulation with Private Attributes | class foo:
def __init__(self,a,b):
self.a = a
self.b = b
def add(self):
return self.a + self.b
foo_object = foo(3,4)
foo_object.add()
foo_object.a = 6
foo_object.add()
class foo:
def __init__(self,a,b):
self._a = a
self._b = b
def add(self):
return self._a + self._b
foo_object = foo(3,4)
foo_object.add()
foo_object.a = 6
foo_object.add() | _____no_output_____ | Apache-2.0 | OOP_58001_OOP_Concepts_2.ipynb | AndreiBenavidez/OOP-58001 |
Encapsulation by mangling with double underscores | class Counter:
def __init__(self):
self.current = 0
def increment(self):
self.current += 1 #current = current+1
def value(self):
return self.current
def reset(self):
self.current = 0
counter = Counter()
counter.increment()
counter.increment()
counter.increment()
print(counter.value()) | 3
| Apache-2.0 | OOP_58001_OOP_Concepts_2.ipynb | AndreiBenavidez/OOP-58001 |
Inheritance | class Person:
def __init__(self,fname,sname):
self.fname = fname
self.sname = sname
def printname(self):
print(self.fname,self.sname)
x = Person("Andrei","Benavidez")
x.printname()
class Teacher(Person):
pass
x = Teacher("Drei", "Benavidez")
x.printname() | Andrei Benavidez
Drei Benavidez
| Apache-2.0 | OOP_58001_OOP_Concepts_2.ipynb | AndreiBenavidez/OOP-58001 |
Polymorphism | class RegularPolygon:
def __init__(self,side):
self._side = side
class Square(RegularPolygon):
def area(self):
return self._side * self._side
class EquilateralTriangle(RegularPolygon):
def area(self):
return self._side * self._side * 0.433
obj1 = Square(4)
obj2 = EquilateralTriangle(3)
obj1.area()
obj2.area() | _____no_output_____ | Apache-2.0 | OOP_58001_OOP_Concepts_2.ipynb | AndreiBenavidez/OOP-58001 |
* Create a Python program that displays the name of 3 students (Student 1, Student 2, Student 3) and their grades* Create a class name "Person" and attributes - std1, std2, std3, pre, mid, fin* Compute for the average grade of each term using Grade() method* Information about student's grades must be hidden from others | import random
class Person:
def __init__ (self, student, pre, mid, fin):
self.student = student
self.pre = pre *0.30
self.mid = mid *0.30
self.fin = fin *0.40
def Grade (self):
print (self.student, "has an average grade of", self.pre, "in Prelims")
print (self.student, "has an average grade of", self.mid, "in Midterms")
print (self.student, "has an average grade of", self.fin, "in Finals")
std1 = Person ("Andrei", random.randint(70,100), random.randint(70,100), random.randint(70,100))
std2 = Person ("Ady", random.randint(70,100), random.randint(70,100), random.randint(70,100))
std3 = Person ("Drei", random.randint(70,100), random.randint(70,100), random.randint(70,100))
std1.Grade()
std2.Grade()
std3.Grade() | Andrei has an average grade of 21.9 in Prelims
Andrei has an average grade of 23.4 in Midterms
Andrei has an average grade of 33.2 in Finals
Ady has an average grade of 26.4 in Prelims
Ady has an average grade of 27.599999999999998 in Midterms
Ady has an average grade of 28.0 in Finals
Drei has an average grade of 28.2 in Prelims
Drei has an average grade of 29.099999999999998 in Midterms
Drei has an average grade of 38.800000000000004 in Finals
| Apache-2.0 | OOP_58001_OOP_Concepts_2.ipynb | AndreiBenavidez/OOP-58001 |
Part 1.1 基于枚举方法来搭建中文分词工具（新）此项目需要的数据：1. 综合类中文词库.xlsx： 包含了中文词，当做词典来用2. 以变量的方式提供了部分unigram概率 word_prob举个例子： 给定词典=[我们 学习 人工 智能 人工智能 未来 是]， 另外我们给定unigram概率：p(我们)=0.25, p(学习)=0.15, p(人工)=0.05, p(智能)=0.1, p(人工智能)=0.2, p(未来)=0.1, p(是)=0.15 Step 1: 对于给定字符串：”我们学习人工智能，人工智能是未来“, 找出所有可能的分割方式- [我们，学习，人工智能，人工智能，是，未来]- [我们，学习，人工，智能，人工智能，是，未来]- [我们，学习，人工，智能，人工，智能，是，未来]- [我们，学习，人工智能，人工，智能，是，未来]....... Step 2: 我们也可以计算出每一个切分之后句子的概率- p(我们，学习，人工智能，人工智能，是，未来)= -log p(我们)-log p(学习)-log p(人工智能)-log p(人工智能)-log p(是)-log p(未来)- p(我们，学习，人工，智能，人工智能，是，未来)=-log p(我们)-log p(学习)-log p(人工)-log p(智能)-log p(人工智能)-log p(是)-log p(未来)- p(我们，学习，人工，智能，人工，智能，是，未来)=-log p(我们)-log p(学习)-log p(人工)-log p(智能)-log p(人工)-log p(智能)-log p(是)-log p(未来)- p(我们，学习，人工智能，人工，智能，是，未来)=-log p(我们)-log p(学习)-log p(人工智能)-log p(人工)-log p(智能)-log(是)-log p(未来)..... Step 3: 返回第二步中概率最大的结果 | import pandas as pd
import numpy as np
path = "./data/综合类中文词库.xlsx"
data_frame = pd.read_excel(path, header = None)
dic_word_list = data_frame[data_frame.columns[0]].tolist()
dic_words = dic_word_list # 保存词典库中读取的单词
# 以下是每一个单词出现的概率。为了问题的简化，我们只列出了一小部分单词的概率。 在这里没有出现的的单词但是出现在词典里的，统一把概率设置成为0.00001
# 比如 p("学院")=p("概率")=...0.00001
word_prob = {"北京":0.03,"的":0.08,"天":0.005,"气":0.005,"天气":0.06,"真":0.04,"好":0.05,"真好":0.04,"啊":0.01,"真好啊":0.02,
"今":0.01,"今天":0.07,"课程":0.06,"内容":0.06,"有":0.05,"很":0.03,"很有":0.04,"意思":0.06,"有意思":0.005,"课":0.01,
"程":0.005,"经常":0.08,"意见":0.08,"意":0.01,"见":0.005,"有意见":0.02,"分歧":0.04,"分":0.02, "歧":0.005}
for item in dic_words:
word_prob.setdefault(item, 0.00001)
def split_word_with_dic_front_max(dic=[], input_str=""):
'''前项最大分词'''
input_str_tmp = input_str
segments = []
while(input_str_tmp!=""):
for i in range(len(input_str_tmp), -1, -1):
word = input_str_tmp[:i]
if word in dic:
segments.append(word)
input_str_tmp = input_str_tmp[len(word):]
break
return segments
def split_word_with_dic_front_min(dic=[], input_str=""):
'''前项最小分词'''
input_str_tmp = input_str
segments = []
while(input_str_tmp!=""):
for i in range(len(input_str_tmp)):
word = input_str_tmp[:i] if len(input_str_tmp) != 1 else input_str_tmp[0] # 防止为空时找不到索引而循环
if word in dic:
segments.append(word)
input_str_tmp = input_str_tmp[len(word):]
break
return segments
def split_word_with_dic_back_max(dic=[], input_str=""):
'''后项最大分词'''
input_str_tmp = input_str
segments = []
while(input_str_tmp!=""):
for i in range(len(input_str_tmp)):
word = input_str_tmp[i:]
if word in dic:
segments.append(word)
input_str_tmp = input_str_tmp[:-len(word)]
break
return segments[::-1]
def split_word_with_dic_back_min(dic=[], input_str=""):
'''后项最小分词'''
input_str_tmp = input_str
segments = []
while(input_str_tmp!=""):
for i in range(len(input_str_tmp), -1, -1):
word = input_str_tmp[i:]
if word in dic:
segments.append(word)
input_str_tmp = input_str_tmp[:-len(word)]
break
return segments[::-1]
def split_word(dic=[], input=""):
tmp_result = []
tmp_result.append(split_word_with_dic_front_max(dic, input))
tmp_result.append(split_word_with_dic_back_max(dic, input))
tmp_result.append(split_word_with_dic_front_min(dic, input))
tmp_result.append(split_word_with_dic_back_min(dic, input))
return tmp_result
def get_split_probability_use_segment(split_word_segment=[]):
sum_result = 0
for seg in split_word_segment:
sum_result -= np.log(word_prob.get(seg))
return sum_result
def get_split_probability(split_word_segments=[[], ]):
'''
根据传入的分词结果计算出概率最高的分词结果并返回
'''
index = 0
max_index = 0
sum = get_split_probability_use_segment(split_word_segments[0])
for segment in split_word_segments:
tmp = get_split_probability_use_segment(segment)
if(sum>tmp):
sum = tmp
max_index = index
index += 1
return split_word_segments[max_index], sum
# 分数（10）
## TODO 请编写word_segment_naive函数来实现对输入字符串的分词
def word_segment_naive(input_str):
"""
1. 对于输入字符串做分词，并返回所有可行的分词之后的结果。
2. 针对于每一个返回结果，计算句子的概率
3. 返回概率最高的最作为最后结果
input_str: 输入字符串 输入格式：“今天天气好”
best_segment: 最好的分词结果 输出格式：["今天"，"天气"，"好"]
"""
# TODO： 第一步： 计算所有可能的分词结果，要保证每个分完的词存在于词典里，这个结果有可能会非常多。
segments = split_word(list(word_prob.keys()), input_str) # 存储所有分词的结果。如果次字符串不可能被完全切分，则返回空列表(list)
# 格式为：segments = [["今天"，“天气”，“好”],["今天"，“天“，”气”，“好”],["今“，”天"，“天气”，“好”],...]
# TODO: 第二步：循环所有的分词结果，并计算出概率最高的分词结果，并返回
best_segment, best_score = get_split_probability(segments)
return best_segment
# 测试
print(word_segment_naive("北京的天气真好啊"))
print(word_segment_naive("今天的课程内容很有意思"))
print(word_segment_naive("经常有意见分歧")) | ['北京', '的', '天气', '真好啊']
['今天', '的', '课程', '内容', '很有', '意思']
['经常', '有意见', '分歧']
| Apache-2.0 | enumerate.ipynb | chmoe/NLPLearning-CNWordSegmentation |
1 Logistic Regression 1.1 Visualizing the data | import matplotlib.pyplot as plt
import numpy as np
from numpy import genfromtxt
data = genfromtxt('data/ex2data1.txt', delimiter=',')
# Print first five rows to see what it looks like
print(data[:5, :])
X = data[:, 0:2] # scores on test1, test2
Y = data[:, 2] # admitted yes/no
print(X[:5])
print(Y[:5])
plt.figure(figsize=(10, 7))
plt.scatter(X[Y==1, 0], X[Y==1, 1], c='g', marker='P')
plt.scatter(X[Y==0, 0], X[Y==0, 1], c='r', marker='o')
plt.xlabel('Exam 1 score')
plt.ylabel('Exam 2 score')
plt.legend(['Admitted','Not admitted'])
plt.show() | _____no_output_____ | MIT | ex2/2_1_Logistic_regression.ipynb | surajsimon/Andrew-ng-machine-learning-course-python-implementation |
1.2 Implementation 1.2.1 Warmup exercise: sigmoid function | import math
def sigmoid(z):
g = 1. / (1. + math.exp(-z))
return g
# Vectorize sigmoid function so it works on all elements of a numpy array
sigmoid = np.vectorize(sigmoid)
# Test sigmoid function
test = np.array([[0]])
sigmoid(test)
sigmoid(0)
test = np.array([[-10,-1], [0,0], [1,10]])
sigmoid(test) | _____no_output_____ | MIT | ex2/2_1_Logistic_regression.ipynb | surajsimon/Andrew-ng-machine-learning-course-python-implementation |
1.2.2 Cost function and gradient | # Setup the data matrix appropriately, and add ones for the intercept term
[m, n] = X.shape
# Add intercept term to X
X = np.column_stack((np.ones(m), X))
# Initialize fitting parameters
initial_theta = np.zeros([n + 1, 1])
def costFunction(theta, X, y):
# Cost
J = 0
m = len(y)
for i in range(m):
z = np.dot(theta.T, X[i])
J += -y[i]*math.log(sigmoid(z)) - (1 - y[i])*math.log((1 - sigmoid(z)))
J = J/m
# Gradient
grad = np.zeros(theta.shape)
for j in range(X.shape[1]):
for i in range(m):
z = np.dot(theta.T, X[i])
grad[j] += (sigmoid(z) - y[i]) * X[i,j]
grad[j] = grad[j]/m
return J, grad
# Compute and display initial cost and gradient
cost, grad = costFunction(initial_theta, X, Y)
print('Cost at initial theta (zeros):\n', cost)
print('Expected cost (approx):\n 0.693\n')
print('Gradient at initial theta (zeros):\n', grad)
print('\nExpected gradients (approx):\n -0.1000\n -12.0092\n -11.2628\n')
# Compute and display cost and gradient with non-zero theta
test_theta = np.array([-24, 0.2, 0.2])
cost, grad = costFunction(test_theta, X, Y)
print('Cost at test theta (zeros):\n', cost)
print('Expected cost (approx):\n 0.218\n')
print('Gradient at test theta (zeros):\n', grad)
print('\nExpected gradients (approx):\n 0.043\t 2.566\t 2.647') | Cost at test theta (zeros):
0.218330193827
Expected cost (approx):
0.218
Gradient at test theta (zeros):
[ 0.04290299 2.56623412 2.64679737]
Expected gradients (approx):
0.043 2.566 2.647
| MIT | ex2/2_1_Logistic_regression.ipynb | surajsimon/Andrew-ng-machine-learning-course-python-implementation |
1.2.3 Learning parameters using fminunc We're supposed to use Octave's ```fminunc``` function for this. I can't find a python implementation of this, so let's use ```scipy.optimize.minimize(method='TNC')``` instead. | from scipy.optimize import minimize
res = minimize(fun=costFunction, x0=initial_theta, args=(X, Y), method='TNC', jac=True, options={'maxiter':400})
res
theta = res.x
print('Cost at theta found by fmin_tnc:\n', res.fun)
print('Expected cost (approx):\n 0.203\n')
print('Theta:\n', res.x)
print('Expected theta (approx):\n -25.161\t 0.206\t 0.201')
def plotDecisionBoundary(theta, X, Y):
plt.figure(figsize=(10, 7))
plt.scatter(X[Y==1, 1], X[Y==1, 2], c='g', marker='P')
plt.scatter(X[Y==0, 1], X[Y==0, 2], c='r', marker='o')
plt.xlabel('Exam 1 score')
plt.ylabel('Exam 2 score')
plot_x = [min(X[:,1]-2), max(X[:,1])+2]
plot_y = [(-1/theta[2])*(theta[1]*plot_x[0] + theta[0]), (-1/theta[2])*(theta[1]*plot_x[1] + theta[0])]
plt.plot(plot_x, plot_y)
plt.xlim(min(X[:,1]-2),max(X[:,1])+2)
plt.ylim(min(X[:,2]-2),max(X[:,2])+2)
plt.legend(['Decision boundary', 'Admitted', 'Not admitted'])
plt.show()
plotDecisionBoundary(theta, X, Y) | _____no_output_____ | MIT | ex2/2_1_Logistic_regression.ipynb | surajsimon/Andrew-ng-machine-learning-course-python-implementation |
1.2.4 Evaluating logistic regression | # Predict probability of admission for a student with score 45 on exam 1 and score 85 on exam 2
prob = sigmoid(np.dot([1, 45, 85], theta))
print('For a student with scores 45 and 85, we predict an admission probability of:\n', prob)
print('Expected value:\n 0.775 +/- 0.002\n\n')
# Compute accuracy on our training set
def predict(theta, X):
m = X.shape[0] # Number of training examples
p = np.zeros(m)
for i in range(m):
prob = sigmoid(np.dot(X[i,:], theta))
if prob >= 0.5:
p[i] = 1 # Predict "Admitted" if prob >= 0.5
return p
p = predict(theta, X)
accuracy = sum(p==Y) / m
print('Training accuracy:\n', accuracy * 100, '%')
print('Expected accuracy (approx):\n 89.0 %\n') | Training accuracy:
89.0 %
Expected accuracy (approx):
89.0 %
| MIT | ex2/2_1_Logistic_regression.ipynb | surajsimon/Andrew-ng-machine-learning-course-python-implementation |
zip(df2, axes.flatten()) | fig, axes = plt.subplots(8,2)
x= groups['q-value']
fig, axes = plt.subplots(6,6, figsize=(12,12), sharex=True)
#axr = axes.ravel()
#zip(groups, axes.flatten())
for ax, x in zip(axes.flat, x):
sb.distplot(x[1], ax=ax)
ax.set_title(x[0])
ax.axvline(0.05, color='r', ls=':')
#axes.flat[-1].set_visible(False)
ax.set_xlim(0,1)
plt.tight_layout()
fig, ax = plt.subplots(1, 1)
print(x[1])
sb.distplot(x[1]['q-value'], hist=False, ax=ax, )
ax.set_title(x[0])
ax.axvline(0.05, color='r', ls=':')
plt.gca()
plt.show()
x= groups['p-value']
fig, axes = plt.subplots(6,6, figsize=(12,12), sharex=True)
#axr = axes.ravel()
#zip(groups, axes.flatten())
for ax, x in zip(axes.flat, x):
sb.distplot(x[1], ax=ax)
ax.set_title(x[0])
ax.axvline(0.05, color='r', ls=':')
#axes.flat[-1].set_visible(False)
ax.set_xlim(0,0.001)
plt.tight_layout() | _____no_output_____ | MIT | notebook/distribution_qvals_dmmpmm.ipynb | isabelleberger/isabelle- |
Example notebook that does stuff with the output files from a xspec, namely:* the .txt from wdata that saves the data/model,* the *.fits from writefits that save out the fit parameters.IGH 14 Feb 2020 - Started IGH 20 Feb 2020 - Better latex font, and fancier error label | from astropy.io import fits
import numpy as np
import matplotlib.pyplot as plt
import matplotlib
import warnings
warnings.simplefilter('ignore')
# Some useful parameters
# norm = 1e-14/(4piD_A^2)*\int n_e n_p dV
# The norm factor from the XSPEC APEC model is defined here: https://heasarc.gsfc.nasa.gov/xanadu/xspec/manual/node134.html
kev2mk=0.0861733
emfact=3.5557e-42
# An example file produced from writefits using FPMA and FPMA and a model of const*apec
hdumok = fits.open('mod_thf2prb.fits')
# hdumok.info()
mokprm=hdumok[1].data
mokcol=hdumok[1].columns.names
hdumok.close()
print(mokcol)
t1=mokprm['kt2'][0]/kev2mk
t1_cr=mokprm['ekt2'][0]/kev2mk
print('T1: ',t1,'MK, Err Rng: ',t1_cr)
em1=np.double(mokprm['norm5'][0])/emfact
em1_cr=np.double(mokprm['enorm5'][0])/emfact
print('EM1: ',em1,'cm^-3, Err Rng: ',em1_cr)
fac=mokprm['factor6'][0]
fac_cr=mokprm['efactor6'][0]
print('Fac: ',fac,' Fac Rng: ',fac_cr)
# An example file produced from wdata from an iplot ldata ufspec delchi
dd=[]
with open('mod_thf2prb.txt', 'r') as f:
lines = f.readlines()
dd.append(lines)
# Get's rid of the first 3 lines which are normally not the data
dd=dd[0][3:]
# Different plots separated by 'NO NO NO NO NO\n' so need to find where this occurs
id_break=[i for i, value in enumerate(dd) if value == 'NO NO NO NO NO\n']
dd_ld=dd[:id_break[0]]
dd_uf=dd[id_break[0]+1:id_break[1]]
dd_dc=dd[id_break[1]+1:]
# For this example just assign the ldata plot
eng_ld=[]
deng_ld=[]
data_ld=[]
edata_ld=[]
mod_ld=[]
for i in dd_ld:
temp_ld=i.split()
eng_ld.append(float(temp_ld[0]))
deng_ld.append(float(temp_ld[1]))
data_ld.append(float(temp_ld[2]))
edata_ld.append(float(temp_ld[3]))
mod_ld.append(float(temp_ld[4]))
eng_ld=np.array(eng_ld)
deng_ld=np.array(deng_ld)
data_ld=np.array(data_ld)
edata_ld=np.array(edata_ld)
mod_ld=np.array(mod_ld)
# # Setup the font used for plotting
matplotlib.rcParams['font.sans-serif'] = "Arial"
matplotlib.rcParams['font.family'] = "sans-serif"
matplotlib.rcParams['font.size'] = 18
matplotlib.rcParams['mathtext.default']="regular"
# A lot of this is just to get the plot exactly how I want it
fig,axs=plt.subplots(2,1,figsize=(7,10),gridspec_kw=dict( height_ratios=[4,1],hspace=0.05))
# Plot the data and model fit on the top plot
axs[0].semilogy(eng_ld,data_ld,'.',ms=0.5,color='k')
for i in np.arange(len(data_ld)):
axs[0].plot([eng_ld[i],eng_ld[i]],[data_ld[i]-edata_ld[i],data_ld[i]+edata_ld[i]],color='k')
axs[0].plot([eng_ld[i]-deng_ld[i],eng_ld[i]+deng_ld[i]],[data_ld[i],data_ld[i]],color='k')
axs[0].plot(eng_ld,mod_ld,color='firebrick',drawstyle='steps-mid')
axs[0].set_ylabel('NuSTAR count s$^{-1}$ keV$^{-1}$')
ylim=[2e-1,7e4]
xlim=[2,9]
axs[0].set_ylim(ylim)
for aa in axs:
aa.set_xlim(xlim)
aa.label_outer()
# Put the actual fit values on the top plot
param_labelt="{0:5.3f} ({1:5.3f} - {2:5.3f}) MK ".format(t1,t1_cr[0],t1_cr[1])
param_labelem="{0:5.2e} ({1:5.2e} - {2:5.2e}) ".format(em1,em1_cr[0],em1_cr[1])+"$cm^{-3}$"
axs[0].text(0.95,0.92,param_labelt,color='firebrick',ha='right',transform=axs[0].transAxes)
axs[0].text(0.95,0.86,param_labelem,color='firebrick',ha='right',transform=axs[0].transAxes)
axs[0].text(0.95,0.80,"{0:4.2f}".format(fac),color='k',ha='right',transform=axs[0].transAxes)
# You need to specify this yourself
fiter=[2.5,7.0]
axs[0].plot([fiter[0],fiter[0]],[ylim[0],10**(0.8*np.log10(ylim[1]))],':',color='grey')
axs[0].plot([fiter[1],fiter[1]],[ylim[0],10**(0.8*np.log10(ylim[1]))],':',color='grey')
# Calculate and plot the residuals on the bottom plot
resid=(data_ld-mod_ld)/edata_ld
axs[1].plot(eng_ld,resid,'.',ms=0.5,color='k')
axs[1].set_ylim([-4,4])
axs[1].set_xlabel('Energy [keV]')
axs[1].plot(xlim,[0,0],'--',color='grey')
for i in np.arange(len(data_ld)):
axs[1].plot([eng_ld[i]-deng_ld[i],eng_ld[i]+deng_ld[i]],[resid[i],resid[i]],color='firebrick')
axs[1].set_ylabel('Resid')
plt.show()
# Same as above but just a fancier way of plotting the error ranges
fig,axs=plt.subplots(2,1,figsize=(7,10),gridspec_kw=dict( height_ratios=[4,1],hspace=0.05))
# Plot the data and model fit on the top plot
axs[0].semilogy(eng_ld,data_ld,'.',ms=0.5,color='k')
for i in np.arange(len(data_ld)):
axs[0].plot([eng_ld[i],eng_ld[i]],[data_ld[i]-edata_ld[i],data_ld[i]+edata_ld[i]],color='k')
axs[0].plot([eng_ld[i]-deng_ld[i],eng_ld[i]+deng_ld[i]],[data_ld[i],data_ld[i]],color='k')
axs[0].plot(eng_ld,mod_ld,color='firebrick',drawstyle='steps-mid')
axs[0].set_ylabel('NuSTAR count s$^{-1}$ keV$^{-1}$')
ylim=[2e-1,7e4]
xlim=[2,9]
axs[0].set_ylim(ylim)
for aa in axs:
aa.set_xlim(xlim)
aa.label_outer()
# Put the actual fit values on the top plot
labt="{0:5.3f}".format(t1)
labtup="{0:5.3f}".format(t1_cr[1]-t1)
labtlw="{0:5.3f}".format(t1-t1_cr[0])
powem1=np.floor(np.log10(em1))
labpowem1="{0:2d}".format(int(powem1))
labem="{0:5.2f}".format(em1/10**powem1)
labemup="{0:5.2f}".format((em1_cr[1]-em1)/10**powem1)
labemlw="{0:5.2f}".format((em1-em1_cr[0])/10**powem1)
labf="{0:5.2f}".format(fac)
labfup="{0:5.2f}".format(fac_cr[1]-fac)
labflw="{0:5.2f}".format(fac-fac_cr[0])
axs[0].text(0.02,0.92,labt+" $^{+"+labtup+"}_{-"+labtlw+"}\;MK,$",color='firebrick',ha='left',transform=axs[0].transAxes)
axs[0].text(0.34,0.92,labem+" $^{+"+labemup+"}_{-"+labemlw+"}\,×\,10^{"+labpowem1+"}\;cm^{-3},$",color='firebrick',ha='left',transform=axs[0].transAxes)
axs[0].text(0.78,0.92,labf+" $^{+"+labfup+"}_{-"+labflw+"}$",color='k',ha='left',transform=axs[0].transAxes)
# You need to specify this yourself
fiter=[2.5,7.0]
axs[0].plot([fiter[0],fiter[0]],[ylim[0],10**(0.8*np.log10(ylim[1]))],':',color='grey')
axs[0].plot([fiter[1],fiter[1]],[ylim[0],10**(0.8*np.log10(ylim[1]))],':',color='grey')
# Calculate and plot the residuals on the bottom plot
resid=(data_ld-mod_ld)/edata_ld
axs[1].plot(eng_ld,resid,'.',ms=0.5,color='k')
axs[1].set_ylim([-4,4])
axs[1].set_xlabel('Energy [keV]')
axs[1].plot(xlim,[0,0],'--',color='grey')
for i in np.arange(len(data_ld)):
axs[1].plot([eng_ld[i]-deng_ld[i],eng_ld[i]+deng_ld[i]],[resid[i],resid[i]],color='firebrick')
axs[1].set_ylabel('Resid')
plt.show()
| _____no_output_____ | MIT | xspec/example_xspec.ipynb | ianan/nsigh |
Inverse Kinematics OptimizationThe previous doc explained features and how they define objectives of a constrained optimization problem. Here we show how to use this to solve IK optimization problems.At the bottom there is more general text explaining the basic concepts. Demo of features in Inverse KinematicsLet's setup a standard configuration. (Lock the window with "Always on Top".) | import sys
sys.path.append('../build') #rai/lib')
import numpy as np
import libry as ry
C = ry.Config()
C.addFile('../rai-robotModels/pr2/pr2.g')
C.addFile('../rai-robotModels/objects/kitchen.g')
C.view() | _____no_output_____ | MIT | tutorials/3-IK-optimization.ipynb | Zwoelf12/rai-python |
For simplicity, let's add a frame that represents goals | goal = C.addFrame("goal")
goal.setShape(ry.ST.sphere, [.05])
goal.setColor([.5,1,1])
goal.setPosition([1,.5,1])
X0 = C.getFrameState() #store the initial configuration | _____no_output_____ | MIT | tutorials/3-IK-optimization.ipynb | Zwoelf12/rai-python |
We create an IK engine. The only objective is that the `positionDiff` (position difference in world coordinates) between `pr2L` (the yellow blob in the left hand) and `goal` is equal to zero: | IK = C.komo_IK(False)
IK.addObjective(type=ry.OT.eq, times = [1,2], feature=ry.FS.positionDiff, frames=['pr2L', 'goal']) | _____no_output_____ | MIT | tutorials/3-IK-optimization.ipynb | Zwoelf12/rai-python |
We now call the optimizer (True means with random initialization/restart). | IK.optimize()
IK.getReport() | ** KOMO::run solver:dense collisions:0 x-dim:25 T:1 k:1 phases:1 stepsPerPhase:1 tau:1 #timeSlices:2 #totalDOFs:25 #frames:358
** optimization time:0.00914103 (kin:0.000131 coll:0.000132 feat:0 newton: 0.00105) setJointStateCount:35
sos:0.0808073 ineq:0 eq:0.238354
| MIT | tutorials/3-IK-optimization.ipynb | Zwoelf12/rai-python |
The best way to retrieve the result is to copy the optimized IK configuration back into your working configuration C, which is now also displayed | #IK.getFrameState(1)
C.setFrameState(IK.getFrameState(0)) | _____no_output_____ | MIT | tutorials/3-IK-optimization.ipynb | Zwoelf12/rai-python |
We can redo the optimization, but for a different configuration, namely a configuration where the goal is in another location.For this we move goal in our working configuration C, then copy C back into the IK engine's configurations: | ## (iterate executing this cell for different goal locations!)
# move goal
goal.setPosition([.8,.2,.5])
# copy C into the IK's internal configuration(s)
IK.setConfigurations(C)
# reoptimize
IK.optimize(0.) # 0: no adding of noise for a random restart
#print(IK.getReport())
print(np.shape(IK.getFrameState(0)))
print(np.shape(IK.getFrameState(0)[1]))
# grab result
# C.setFrameState( IK.getConfiguration(1) )
C.setFrameState(IK.getFrameState(0)) | ** KOMO::run solver:dense collisions:0 x-dim:25 T:1 k:1 phases:1 stepsPerPhase:1 tau:1 #timeSlices:2 #totalDOFs:25 #frames:358
** optimization time:0.000305789 (kin:0.000238 coll:0.000149 feat:0 newton: 0.001415) setJointStateCount:3
sos:0.000285026 ineq:0 eq:0.0270084
(179, 7)
(7,)
| MIT | tutorials/3-IK-optimization.ipynb | Zwoelf12/rai-python |
Let's solve some other problems, always creating a novel IK engine:The relative position of `goal` in `pr2R` coordinates equals [0,0,-.2] (which is 20cm straight in front of the yellow blob) | C.setFrameState(X0)
IK = C.komo_IK(False)
IK.addObjective(type=ry.OT.eq,times=[1], feature=ry.FS.positionRel, frames=['goal','pr2R'], target=[0,0,-.2])
IK.optimize()
C.setFrameState(IK.getFrameState(0)) | ** KOMO::run solver:dense collisions:0 x-dim:25 T:1 k:1 phases:1 stepsPerPhase:1 tau:1 #timeSlices:2 #totalDOFs:25 #frames:358
** optimization time:0.00105824 (kin:5.2e-05 coll:1.1e-05 feat:0 newton: 0.000124) setJointStateCount:12
sos:0.00848536 ineq:0 eq:0.0341739
| MIT | tutorials/3-IK-optimization.ipynb | Zwoelf12/rai-python |
The distance between `pr2R` and `pr2L` is zero: | C.setFrameState(X0)
IK = C.komo_IK(False)
IK.addObjective(type=ry.OT.eq, times=[1], feature=ry.FS.distance, frames=['pr2L','pr2R'])
IK.optimize()
C.setFrameState(IK.getFrameState(0)) | ** KOMO::run solver:dense collisions:0 x-dim:25 T:1 k:1 phases:1 stepsPerPhase:1 tau:1 #timeSlices:2 #totalDOFs:25 #frames:358
** optimization time:0.00069327 (kin:3.3e-05 coll:5e-06 feat:0 newton: 5.9e-05) setJointStateCount:6
sos:0.00209253 ineq:0 eq:0.0149894
| MIT | tutorials/3-IK-optimization.ipynb | Zwoelf12/rai-python |
The 3D difference between the z-vector of `pr2R` and the z-vector of `goal`: | C.setFrameState(X0)
IK = C.komo_IK(False)
IK.addObjective(type=ry.OT.eq, times=[1], feature=ry.FS.vectorZDiff, frames=['pr2R', 'goal'])
IK.optimize()
C.setFrameState(IK.getFrameState(0)) | ** KOMO::run solver:dense collisions:0 x-dim:25 T:1 k:1 phases:1 stepsPerPhase:1 tau:1 #timeSlices:2 #totalDOFs:25 #frames:358
** optimization time:0.00144349 (kin:0.000111 coll:2.9e-05 feat:0 newton: 0.000115) setJointStateCount:12
sos:0.0163838 ineq:0 eq:0.0143332
| MIT | tutorials/3-IK-optimization.ipynb | Zwoelf12/rai-python |
The scalar product between the z-vector of `pr2R` and the z-vector of `goal` is zero: | C.setFrameState(X0)
IK = C.komo_IK(False)
IK.addObjective(type=ry.OT.eq, times=[1], feature=ry.FS.scalarProductZZ, frames=['pr2R', 'goal'])
IK.optimize()
C.setFrameState(IK.getFrameState(0)) | ** KOMO::run solver:dense collisions:0 x-dim:25 T:1 k:1 phases:1 stepsPerPhase:1 tau:1 #timeSlices:2 #totalDOFs:25 #frames:358
** optimization time:0.000686185 (kin:7.1e-05 coll:3e-06 feat:0 newton: 4.2e-05) setJointStateCount:4
sos:0.000248896 ineq:0 eq:0.00308733
| MIT | tutorials/3-IK-optimization.ipynb | Zwoelf12/rai-python |
etc etc More explanationsAll methods to compute paths or configurations solve constrained optimization problems. To use them, you need to learn to define constrained optimization problems. Some definitions:* An objective defines either a sum-of-square cost term, or an equality constraint, or an inequality constraint in the optimization problem. An objective is defined by its type and its feature. The type can be `sos` (sum-of-squares), `eq`, or `ineq`, referring to the three cases.* A feature is a (differentiable mapping) from the decision variable (the full path, or robot configuration) to a feature space. If the feature space is, e.g., 3-dimensional, this defines 3 sum-of-squares terms, or 3 inequality, or 3 equality constraints, one for each dimension. For instance, the feature can be the 3-dim robot hand position in the 15th time slice of a path optimization problem. If you put an equality constraint on this feature, then this adds 3 equality constraints to the overall path optimization problem.* A feature is defined by the keyword for the feature map (e.g., `pos` for position), typically by a set of frame names that tell which objects we refer to (e.g., `pr2L` for the left hand of the pr2), optionally some modifiers (e.g., a scale or target, which linearly transform the feature map), and the set of configuration IDs or time slices the feature is to be computed from (e.g., `confs=[15]` if the feat is to be computed from the 15th time slice in a path optimization problem).* In path optimization problems, we often want to add objectives for a whole time interval rather than for a single time slice or specific configuration. E.g., avoid collisions from start to end. When adding objectives to the optimization problem we can specify whole intervals, with `times=[1., 2.]`, so that the objective is added to each configuration in this time interval.* Some features, especially velocity and acceleration, refer to a tuple of (consecutive) configurations. E.g., when you impose an acceleration feature, you need to specify `confs=[13, 14, 15]`. Or if you use `times=[1.,1.]`, the acceleration features is computed from the configuration that corresponds to time=1 and the two configurations *before*.* All kinematic feature maps (that depend on only one configuration) can be modified to become a velocity or acceleration features. E.g., the position feature map can be modified to become a velocity or acceleration feature.* The `sos`, `eq`, and `ineq` always refer to the feature map to be *zero*, e.g., constraining all features to be equal to zero with `eq`. This is natural for many features, esp. when they refer to differences (e.g. `posDiff` or `posRel`, which compute the relative position between two frames). However, when one aims to constrain the feature to a non-zero constant value, one can modify the objective with a `target` specification.* Finally, all features can be rescaled with a `scale` specification. Rescaling changes the costs that arise from `sos` objectives. Rescaling also has significant impact on the convergence behavior for `eq` and `ineq` constraints. As a guide: scale constraints so that if they *would* be treated as squared penalties (squaredPenalty optim mode, to be made accessible) convergence to reasonable approximate solutions is efficient. Then the AugLag will also converge efficiently to precise constraints. | # Designing a cylinder grasp
D=0
C=0
import sys
sys.path.append('../build') #rai/lib')
import numpy as np
import libry as ry
C = ry.Config()
C.addFile('../rai-robotModels/pr2/pr2.g')
C.addFile('../rai-robotModels/objects/kitchen.g')
C.view()
C.setJointState([.7], ["l_gripper_l_finger_joint"])
C.setJointState( C.getJointState() )
goal = C.addFrame("goal")
goal.setShape(ry.ST.cylinder, [0,0,.2, .03])
goal.setColor([.5,1,1])
goal.setPosition([1.81,.5,1])
X0 = C.getFrameState()
C.setFrameState(X0)
goal.setPosition([1.81,.5,1])
IK = C.komo_IK(False)
IK.addObjective(type=ry.OT.eq, times=[1],feature=ry.FS.positionDiff, frames=['pr2L', 'goal'], scale=[[1,0,0],[0,1,0]])
IK.addObjective(type=ry.OT.ineq, times=[1], feature=ry.FS.positionDiff, frames=['pr2L', 'goal'], scale=[[0,0,1]], target=[0,0,.0005])
IK.addObjective(type=ry.OT.ineq, times=[1], feature=ry.FS.positionDiff, frames=['pr2L', 'goal'], scale=[[0,0,-1]], target=[0,0,-.0005])
IK.addObjective(type=ry.OT.sos, times=[1], feature=ry.FS.scalarProductZZ, frames=['pr2L', 'goal'], scale=[0.1])
IK.addObjective(type=ry.OT.eq, times=[1], feature=ry.FS.scalarProductXZ, frames=['pr2L', 'goal'])
IK.optimize()
C.setFrameState(IK.getFrameState(0))
IK.getReport()
IK.view() | _____no_output_____ | MIT | tutorials/3-IK-optimization.ipynb | Zwoelf12/rai-python |
Welcome to Python FundamentalsIn this module, we are going to establish or review our skills in Python programming. In this notebook we are going to cover:* Variables and Data Types * Operations* Input and Output Operations* Logic Control* Iterables* Functions Variable and Data Types | x = 1
a,b = 0, -1
type(x)
y = 1,0
type(y)
x = float(x)
type(x)
s,t,u ="0", "1", "one"
type(s)
s_int = int(s)
s_int | _____no_output_____ | Apache-2.0 | Activity_1_Python_Fundamentals.ipynb | catherinedrio/Linear-Algebra_ChE_2nd-Sem-2021-2022 |
Operations Arithmetic | a,b,c,d = 2.0, -0.5, 0, -32
### Addition
S = a+b
S
### Subtraction
D = b-d
D
### Multiplication
P = a*d
P
### Division
Q = c/d
Q
### Floor Division
Fq = a//b
Fq
### Exponentiation
E = a**b
E
### Modulo
mod = d%a
mod | _____no_output_____ | Apache-2.0 | Activity_1_Python_Fundamentals.ipynb | catherinedrio/Linear-Algebra_ChE_2nd-Sem-2021-2022 |
Assingment Operations | G, H, J, K = 0, 100, 2, 2
G += a
G
H -= d
H
J *= 2
J
K **= 3
K | _____no_output_____ | Apache-2.0 | Activity_1_Python_Fundamentals.ipynb | catherinedrio/Linear-Algebra_ChE_2nd-Sem-2021-2022 |
Comparators | res_1, res_2, res_3 = 1, 2.0, "1"
true_val = 1.0
## Equality
res_1 == true_val
## Non-equality
res_2 != true_val
## Inequality
t1 = res_1 > res_2
t2 = res_1 < res_2/2
t3 = res_1 >= res_2/2
t4 = res_1 <= res_2
t1 | _____no_output_____ | Apache-2.0 | Activity_1_Python_Fundamentals.ipynb | catherinedrio/Linear-Algebra_ChE_2nd-Sem-2021-2022 |
Logical | res_1 == true_val
res_1 is true_val
res_1 is not true_val
p, q = True, False
conj = p and q
conj
p, q = True, False
disj = p or q
disj
p, q = True, False
nand = not(p and q)
nand
p, q = True, False
xor = (not p and q) or (p and not q)
xor | _____no_output_____ | Apache-2.0 | Activity_1_Python_Fundamentals.ipynb | catherinedrio/Linear-Algebra_ChE_2nd-Sem-2021-2022 |
1/0 | print ("Hello World")
cnt = 1
string = "Hello World"
print(string, ", Current run count is:", cnt)
cnt +=1
print(f"{string}, Current count is {cnt}")
sem_grade = 82.24356457461234
name = "cath"
print("Hello {}, your semestral grade is: {}".format(name, sem_grade))
w_pg, w_mg, w_fg = 0.3, 0.3, 0.4
print("The weights of your semestral grades are:\
\n\t{:.2%} for Prelims\
\n\t{:.2%} for Midterms, and\
\n\t{:.2%} for Finals, ".format(w_pg, w_mg, w_fg))
x = input("enter a number: ")
x
name = input("kimi no nawa: ")
pg = input("Enter prelim grade: ")
mg = input("Enter midterm grade: ")
fg = input("Enter finals grade: ")
sem_grade = None
print("Hello {}, your semestral grade is: {}". format (name, sem_grade)) | kimi no nawa: Cath
Enter prelim grade: 1.00
Enter midterm grade: 1.00
Enter finals grade: 1.00
Hello Cath, your semestral grade is: None
| Apache-2.0 | Activity_1_Python_Fundamentals.ipynb | catherinedrio/Linear-Algebra_ChE_2nd-Sem-2021-2022 |
Looping Statements While | ## while loops
i, j = 0, 10
while(i<=j):
print(f"{i}\t|\t{j}")
i+=1 | 0 | 10
1 | 10
2 | 10
3 | 10
4 | 10
5 | 10
6 | 10
7 | 10
8 | 10
9 | 10
10 | 10
| Apache-2.0 | Activity_1_Python_Fundamentals.ipynb | catherinedrio/Linear-Algebra_ChE_2nd-Sem-2021-2022 |
For | # for(int i=0; i<10; i++){
# printf(i)
# }
i=0
for i in range(11):
print(i)
playlist = ["Crazier", "Bahay-Kubo", "Happier"]
print('Now Playing:\n')
for song in playlist:
print(song) | Now Playing:
Crazier
Bahay-Kubo
Happier
| Apache-2.0 | Activity_1_Python_Fundamentals.ipynb | catherinedrio/Linear-Algebra_ChE_2nd-Sem-2021-2022 |
Flow Control Conditions Statemnents | numeral1, numeral2 = 12, 12
if(numeral1 == numeral2):
print("Yey")
elif(numeral1>numeral2):
print("Hoho")
else:
print("AWW")
print("Hip hip") | Yey
Hip hip
| Apache-2.0 | Activity_1_Python_Fundamentals.ipynb | catherinedrio/Linear-Algebra_ChE_2nd-Sem-2021-2022 |
Functions | [ ] # void DeleteUser(int userid){
# delete(userid);
# }
def delete_user (userid):
print("Successfully deleted user: {}". format(userid))
def delete_all_users ():
print("Successfully deleted all users")
userid = 202011844
delete_user(202011844)
delete_all_users()
def add(addend1, addend2):
print("I know how to add addend1 and addend2")
return addend1 + addend2
def power_of_base2(exponent):
return 2**exponent
addend1 = 5
addend2 = 10
exponent = 5
#add(addend1, addend2)
power_of_base2(exponent) | _____no_output_____ | Apache-2.0 | Activity_1_Python_Fundamentals.ipynb | catherinedrio/Linear-Algebra_ChE_2nd-Sem-2021-2022 |
Grade Calculator Create a grade calculator that computes for the semestral grade of a course. Students could type their names, the name of the course, then their prelim, midterm, and final grade.The program should print the semestral grade in 2 decimal points and should display the following emojis depending on the situation:happy - when grade is greater thann 70.00laughing - wen grade is exactly 70.00sad - when grade is below 70.00...happy, lol, sad - "\U0001F600", "\U0001F606", "\U0001F62D" | w_pg, w_mg, w_fg = 0.3, 0.3, 0.4
name = input("Enter your name: ")
course = input("Enter your course: ")
pg = float(input("Enter prelim grade: "))
mg = float(input("Enter midterm grade: "))
fg = float(input("Enter final grade: "))
sem_grade = (pg*w_pg)+(mg*w_mg)+(fg*w_fg)
print("Hello {} from {}, your semestral grade is: {}" .format(name, course, round(sem_grade,2)))
if(sem_grade > 70.00):
print("\U0001f600")
elif(sem_grade == 70.00):
print("\U0001F606")
else:
print("\U0001F620") | Enter your name: Catherine
Enter your course: BS Chemical Engineering
Enter prelim grade: 97
Enter midterm grade: 98
Enter final grade: 99
Hello Catherine from BS Chemical Engineering, your semestral grade is: 98.1
😀
| Apache-2.0 | Activity_1_Python_Fundamentals.ipynb | catherinedrio/Linear-Algebra_ChE_2nd-Sem-2021-2022 |
Nifti Read ExampleThe purpose of this notebook is to illustrate reading Nifti files and iterating over patches of the volumes loaded from them. | %matplotlib inline
import os
import sys
from glob import glob
import tempfile
import numpy as np
import matplotlib.pyplot as plt
import nibabel as nib
import torch
from torch.utils.data import DataLoader
import monai
from monai.data import NiftiDataset, GridPatchDataset, create_test_image_3d
from monai.transforms import Compose, AddChannel, Transpose, ScaleIntensity, ToTensor, RandSpatialCrop
monai.config.print_config() | MONAI version: 0.1a1.dev8+6.gb3c5761.dirty
Python version: 3.6.9 |Anaconda, Inc.| (default, Jul 30 2019, 19:07:31) [GCC 7.3.0]
Numpy version: 1.18.1
Pytorch version: 1.4.0
Ignite version: 0.3.0
| Apache-2.0 | examples/notebooks/nifti_read_example.ipynb | gml16/MONAI |
Create a number of test Nifti files: | tempdir = tempfile.mkdtemp()
for i in range(5):
im, seg = create_test_image_3d(128, 128, 128)
n = nib.Nifti1Image(im, np.eye(4))
nib.save(n, os.path.join(tempdir, 'im%i.nii.gz'%i))
n = nib.Nifti1Image(seg, np.eye(4))
nib.save(n, os.path.join(tempdir, 'seg%i.nii.gz'%i)) | _____no_output_____ | Apache-2.0 | examples/notebooks/nifti_read_example.ipynb | gml16/MONAI |
Create a data loader which yields uniform random patches from loaded Nifti files: | images = sorted(glob(os.path.join(tempdir, 'im*.nii.gz')))
segs = sorted(glob(os.path.join(tempdir, 'seg*.nii.gz')))
imtrans = Compose([
ScaleIntensity(),
AddChannel(),
RandSpatialCrop((64, 64, 64), random_size=False),
ToTensor()
])
segtrans = Compose([
AddChannel(),
RandSpatialCrop((64, 64, 64), random_size=False),
ToTensor()
])
ds = NiftiDataset(images, segs, transform=imtrans, seg_transform=segtrans)
loader = DataLoader(ds, batch_size=10, num_workers=2, pin_memory=torch.cuda.is_available())
im, seg = monai.utils.misc.first(loader)
print(im.shape, seg.shape) | torch.Size([5, 1, 64, 64, 64]) torch.Size([5, 1, 64, 64, 64])
| Apache-2.0 | examples/notebooks/nifti_read_example.ipynb | gml16/MONAI |
Alternatively create a data loader which yields patches in regular grid order from loaded images: | imtrans = Compose([
ScaleIntensity(),
AddChannel(),
ToTensor()
])
segtrans = Compose([
AddChannel(),
ToTensor()
])
ds = NiftiDataset(images, segs, transform=imtrans, seg_transform=segtrans)
ds = GridPatchDataset(ds, (64, 64, 64))
loader = DataLoader(ds, batch_size=10, num_workers=2, pin_memory=torch.cuda.is_available())
im, seg = monai.utils.misc.first(loader)
print(im.shape, seg.shape)
!rm -rf {tempdir} | _____no_output_____ | Apache-2.0 | examples/notebooks/nifti_read_example.ipynb | gml16/MONAI |
Network Communities Detection In this notebook, we will explore some methods to perform a community detection using several algortihms. Before testing the algorithms, let us create a simple benchmark graph. | %matplotlib inline
from matplotlib import pyplot as plt
import numpy as np
import pandas as pd
import networkx as nx
G = nx.barbell_graph(m1=10, m2=4) | _____no_output_____ | MIT | Chapter05/02_community_detection_algorithms.ipynb | Wapiti08/Graph-Machine-Learning |
Matrix Factorization We start by using some matrix factorization technique to extract the embeddings, which are visualized and then clustered traditional clustering algorithms. | from gem.embedding.hope import HOPE
gf = HOPE(d=4, beta=0.01)
gf.learn_embedding(G)
embeddings = gf.get_embedding()
from sklearn.manifold import TSNE
tsne = TSNE(n_components=2)
emb2d = tsne.fit_transform(embeddings)
plt.plot(embeddings[:, 0], embeddings[:, 1], 'o', linewidth=0) | _____no_output_____ | MIT | Chapter05/02_community_detection_algorithms.ipynb | Wapiti08/Graph-Machine-Learning |
We start by using a GaussianMixture model to perform the clustering | from sklearn.mixture import GaussianMixture
gm = GaussianMixture(n_components=3, random_state=0) #.(embeddings)
labels = gm.fit_predict(embeddings)
colors = ["blue", "green", "red"]
nx.draw_spring(G, node_color=[colors[label] for label in labels]) | _____no_output_____ | MIT | Chapter05/02_community_detection_algorithms.ipynb | Wapiti08/Graph-Machine-Learning |
Spectral Clustering We now perform a spectral clustering based on the adjacency matrix of the graph. It is worth noting that this clustering is not a mutually exclusive clustering and nodes may belong to more than one community | adj=np.array(nx.adjacency_matrix(G).todense())
from communities.algorithms import spectral_clustering
communities = spectral_clustering(adj, k=3) | _____no_output_____ | MIT | Chapter05/02_community_detection_algorithms.ipynb | Wapiti08/Graph-Machine-Learning |
In the next plot we highlight the nodes that belong to a community using the red color. The blue nodes do not belong to the given community | plt.figure(figsize=(20, 5))
for ith, community in enumerate(communities):
cols = ["red" if node in community else "blue" for node in G.nodes]
plt.subplot(1,3,ith+1)
plt.title(f"Community {ith}")
nx.draw_spring(G, node_color=cols) | _____no_output_____ | MIT | Chapter05/02_community_detection_algorithms.ipynb | Wapiti08/Graph-Machine-Learning |
The next command shows the node ids belonging to the different communities | communities | _____no_output_____ | MIT | Chapter05/02_community_detection_algorithms.ipynb | Wapiti08/Graph-Machine-Learning |
Non Negative Matrix Factorization Here, we again use matrix factorization, but now using the Non-Negative Matrix Factorization, and associating the clusters with the latent dimensions. | from sklearn.decomposition import NMF
nmf = NMF(n_components=2)
emb = nmf.fit_transform(adj)
plt.plot(emb[:, 0], emb[:, 1], 'o', linewidth=0) | _____no_output_____ | MIT | Chapter05/02_community_detection_algorithms.ipynb | Wapiti08/Graph-Machine-Learning |
By setting a threshold value of 0.01, we determine which nodes belong to the given community. | communities = [set(np.where(emb[:,ith]>0.01)[0]) for ith in range(2)]
plt.figure(figsize=(20, 5))
for ith, community in enumerate(communities):
cols = ["red" if node in community else "blue" for node in G.nodes]
plt.subplot(1,3,ith+1)
plt.title(f"Community {ith}")
nx.draw_spring(G, node_color=cols) | _____no_output_____ | MIT | Chapter05/02_community_detection_algorithms.ipynb | Wapiti08/Graph-Machine-Learning |
Although the example above does not show this, in general also this clustering method may be non-mutually exclusive, and nodes may belong to more than one community Louvain and Modularity Optimization Here, we use the Louvain method, which is one of the most popular methods for performing community detection, even on fairly large graphs. As described in the chapter, the Louvain method basically optimize the partitioning (it is a mutually exclusing community detection algorithm), identifying the one that maximize the modularity score, meaning that nodes belonging to the same community are very well connected among themself, and weakly connected to the other communities. **Louvain, unlike other community detection algorithms, does not require to specity the number of communities in advance and find the best, optimal number of communities.** | from communities.algorithms import louvain_method
communities = louvain_method(adj)
c = pd.Series({node: colors[ith] for ith, nodes in enumerate(communities) for node in nodes}).values
nx.draw_spring(G, node_color=c)
communities | _____no_output_____ | MIT | Chapter05/02_community_detection_algorithms.ipynb | Wapiti08/Graph-Machine-Learning |
Girvan Newman The Girvan–Newman algorithm detects communities by progressively removing edges from the original graph. The algorithm removes the “most valuable” edge, traditionally the edge with the highest betweenness centrality, at each step. As the graph breaks down into pieces, the tightly knit community structure is exposed and the result can be depicted as a dendrogram.**BE AWARE that because of the betweeness centrality computation, this method may not scale well on large graphs** | from communities.algorithms import girvan_newman
communities = girvan_newman(adj, n=2)
c = pd.Series({node: colors[ith] for ith, nodes in enumerate(communities) for node in nodes}).values
nx.draw_spring(G, node_color=c)
communities | _____no_output_____ | MIT | Chapter05/02_community_detection_algorithms.ipynb | Wapiti08/Graph-Machine-Learning |
ML Pipeline PreparationFollow the instructions below to help you create your ML pipeline. 1. Import libraries and load data from database.- Import Python libraries- Load dataset from database with [`read_sql_table`](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.read_sql_table.html)- Define feature and target variables X and Y | # import libraries
import pandas as pd
from sqlalchemy import create_engine
from sklearn.model_selection import train_test_split, GridSearchCV
from sklearn.multioutput import MultiOutputClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.pipeline import Pipeline, FeatureUnion
from sklearn.metrics import classification_report
from sklearn.feature_extraction.text import CountVectorizer, TfidfTransformer
from nltk.tokenize import word_tokenize
from nltk.stem import WordNetLemmatizer
import pickle
import nltk
nltk.download(['punkt', 'wordnet', 'averaged_perceptron_tagger'])
# load data from database
engine = create_engine('sqlite:///DISASTER.db')
df = pd.read_sql_table("CLEAN_MESSAGES", engine)
X = df['message']
Y = df.iloc[:,4:]
X.head() | _____no_output_____ | MIT | models/ML Pipeline Preparation.ipynb | thiagofuruchima/disaster_message_classification |
2. Write a tokenization function to process your text data | def tokenize(text):
tokens = word_tokenize(text)
lemmatizer = WordNetLemmatizer()
clean_tokens = []
for tok in tokens:
clean_tok = lemmatizer.lemmatize(tok).lower().strip()
clean_tokens.append(clean_tok)
return clean_tokens
| _____no_output_____ | MIT | models/ML Pipeline Preparation.ipynb | thiagofuruchima/disaster_message_classification |
3. Build a machine learning pipelineThis machine pipeline should take in the `message` column as input and output classification results on the other 36 categories in the dataset. You may find the [MultiOutputClassifier](http://scikit-learn.org/stable/modules/generated/sklearn.multioutput.MultiOutputClassifier.html) helpful for predicting multiple target variables. | # create the NLP ML Pipeline
pipeline = Pipeline([
('vect', CountVectorizer(tokenizer=tokenize)),
('tfidf', TfidfTransformer()),
('clf', MultiOutputClassifier(RandomForestClassifier()))
]) | _____no_output_____ | MIT | models/ML Pipeline Preparation.ipynb | thiagofuruchima/disaster_message_classification |
4. Train pipeline- Split data into train and test sets- Train pipeline | # Split the data in train and test sets
X_train, X_test, Y_train, Y_test = train_test_split(X, Y)
# Fit the pipeline
pipeline.fit(X_train, Y_train) | _____no_output_____ | MIT | models/ML Pipeline Preparation.ipynb | thiagofuruchima/disaster_message_classification |
5. Test your modelReport the f1 score, precision and recall for each output category of the dataset. You can do this by iterating through the columns and calling sklearn's `classification_report` on each. | # Predict using the test data
Y_pred = pipeline.predict(X_test)
# Print the classification report for for each column
for i, column in enumerate(Y_train.columns):
print("Columns: ", column)
print(classification_report(Y_test.values[:,i], Y_pred[:,i]))
print() | Columns: related
precision recall f1-score support
0 0.76 0.26 0.39 1539
1 0.80 0.98 0.88 4967
2 1.00 0.04 0.08 48
accuracy 0.80 6554
macro avg 0.86 0.43 0.45 6554
weighted avg 0.80 0.80 0.76 6554
Columns: request
precision recall f1-score support
0 0.89 0.99 0.94 5442
1 0.89 0.40 0.56 1112
accuracy 0.89 6554
macro avg 0.89 0.70 0.75 6554
weighted avg 0.89 0.89 0.87 6554
Columns: offer
precision recall f1-score support
0 1.00 1.00 1.00 6527
1 0.00 0.00 0.00 27
accuracy 1.00 6554
macro avg 0.50 0.50 0.50 6554
weighted avg 0.99 1.00 0.99 6554
Columns: aid_related
precision recall f1-score support
0 0.77 0.89 0.82 3873
1 0.79 0.61 0.69 2681
accuracy 0.77 6554
macro avg 0.78 0.75 0.76 6554
weighted avg 0.78 0.77 0.77 6554
Columns: medical_help
precision recall f1-score support
0 0.93 1.00 0.96 6054
1 0.62 0.05 0.10 500
accuracy 0.93 6554
macro avg 0.77 0.52 0.53 6554
weighted avg 0.90 0.93 0.90 6554
Columns: medical_products
precision recall f1-score support
0 0.95 1.00 0.97 6221
1 0.72 0.05 0.10 333
accuracy 0.95 6554
macro avg 0.84 0.53 0.54 6554
weighted avg 0.94 0.95 0.93 6554
Columns: search_and_rescue
precision recall f1-score support
0 0.97 1.00 0.99 6358
1 0.86 0.03 0.06 196
accuracy 0.97 6554
macro avg 0.91 0.52 0.52 6554
weighted avg 0.97 0.97 0.96 6554
Columns: security
precision recall f1-score support
0 0.98 1.00 0.99 6430
1 0.00 0.00 0.00 124
accuracy 0.98 6554
macro avg 0.49 0.50 0.50 6554
weighted avg 0.96 0.98 0.97 6554
Columns: military
precision recall f1-score support
0 0.97 1.00 0.98 6350
1 0.75 0.03 0.06 204
accuracy 0.97 6554
macro avg 0.86 0.51 0.52 6554
weighted avg 0.96 0.97 0.96 6554
Columns: child_alone
precision recall f1-score support
0 1.00 1.00 1.00 6554
accuracy 1.00 6554
macro avg 1.00 1.00 1.00 6554
weighted avg 1.00 1.00 1.00 6554
Columns: water
precision recall f1-score support
0 0.95 1.00 0.97 6117
1 0.95 0.21 0.34 437
accuracy 0.95 6554
macro avg 0.95 0.60 0.66 6554
weighted avg 0.95 0.95 0.93 6554
Columns: food
precision recall f1-score support
0 0.94 0.99 0.96 5843
1 0.83 0.48 0.61 711
accuracy 0.93 6554
macro avg 0.89 0.73 0.79 6554
weighted avg 0.93 0.93 0.92 6554
Columns: shelter
precision recall f1-score support
0 0.93 1.00 0.96 5978
1 0.85 0.25 0.38 576
accuracy 0.93 6554
macro avg 0.89 0.62 0.67 6554
weighted avg 0.92 0.93 0.91 6554
Columns: clothing
precision recall f1-score support
0 0.98 1.00 0.99 6442
1 0.67 0.05 0.10 112
accuracy 0.98 6554
macro avg 0.83 0.53 0.55 6554
weighted avg 0.98 0.98 0.98 6554
Columns: money
precision recall f1-score support
0 0.98 1.00 0.99 6407
1 1.00 0.02 0.04 147
accuracy 0.98 6554
macro avg 0.99 0.51 0.51 6554
weighted avg 0.98 0.98 0.97 6554
Columns: missing_people
precision recall f1-score support
0 0.99 1.00 0.99 6486
1 1.00 0.01 0.03 68
accuracy 0.99 6554
macro avg 0.99 0.51 0.51 6554
weighted avg 0.99 0.99 0.98 6554
Columns: refugees
| MIT | models/ML Pipeline Preparation.ipynb | thiagofuruchima/disaster_message_classification |
6. Improve your modelUse grid search to find better parameters. | # Define GridSearch parameters
parameters = {'clf__estimator__n_estimators': range(100,200,100),
'clf__estimator__min_samples_split': range(2,3)}
# Instantiate GridSearch object
cv = GridSearchCV(pipeline, param_grid=parameters, n_jobs=4)
# Use GridSearch to find the best parameters
cv.fit(X_train, Y_train) | _____no_output_____ | MIT | models/ML Pipeline Preparation.ipynb | thiagofuruchima/disaster_message_classification |
7. Test your modelShow the accuracy, precision, and recall of the tuned model. Since this project focuses on code quality, process, and pipelines, there is no minimum performance metric needed to pass. However, make sure to fine tune your models for accuracy, precision and recall to make your project stand out - especially for your portfolio! | # Predict using the trained model with the best parameters
Y_pred = cv.predict(X_test)
# Print the classification report for for each column
for i, column in enumerate(Y_train.columns):
print("Columns: ", column)
print(classification_report(Y_test.values[:,i], Y_pred[:,i]))
print() | Columns: related
precision recall f1-score support
0 0.77 0.25 0.38 1539
1 0.80 0.98 0.88 4967
2 1.00 0.04 0.08 48
accuracy 0.80 6554
macro avg 0.86 0.42 0.45 6554
weighted avg 0.80 0.80 0.76 6554
Columns: request
precision recall f1-score support
0 0.89 0.99 0.94 5442
1 0.88 0.42 0.57 1112
accuracy 0.89 6554
macro avg 0.89 0.70 0.75 6554
weighted avg 0.89 0.89 0.88 6554
Columns: offer
precision recall f1-score support
0 1.00 1.00 1.00 6527
1 0.00 0.00 0.00 27
accuracy 1.00 6554
macro avg 0.50 0.50 0.50 6554
weighted avg 0.99 1.00 0.99 6554
Columns: aid_related
precision recall f1-score support
0 0.77 0.88 0.82 3873
1 0.79 0.63 0.70 2681
accuracy 0.78 6554
macro avg 0.78 0.76 0.76 6554
weighted avg 0.78 0.78 0.77 6554
Columns: medical_help
precision recall f1-score support
0 0.93 1.00 0.96 6054
1 0.68 0.06 0.11 500
accuracy 0.93 6554
macro avg 0.80 0.53 0.54 6554
weighted avg 0.91 0.93 0.90 6554
Columns: medical_products
precision recall f1-score support
0 0.95 1.00 0.97 6221
1 0.72 0.05 0.10 333
accuracy 0.95 6554
macro avg 0.84 0.53 0.54 6554
weighted avg 0.94 0.95 0.93 6554
Columns: search_and_rescue
precision recall f1-score support
0 0.97 1.00 0.99 6358
1 0.80 0.02 0.04 196
accuracy 0.97 6554
macro avg 0.89 0.51 0.51 6554
weighted avg 0.97 0.97 0.96 6554
Columns: security
precision recall f1-score support
0 0.98 1.00 0.99 6430
1 0.00 0.00 0.00 124
accuracy 0.98 6554
macro avg 0.49 0.50 0.50 6554
weighted avg 0.96 0.98 0.97 6554
Columns: military
precision recall f1-score support
0 0.97 1.00 0.98 6350
1 0.65 0.05 0.10 204
accuracy 0.97 6554
macro avg 0.81 0.53 0.54 6554
weighted avg 0.96 0.97 0.96 6554
Columns: child_alone
precision recall f1-score support
0 1.00 1.00 1.00 6554
accuracy 1.00 6554
macro avg 1.00 1.00 1.00 6554
weighted avg 1.00 1.00 1.00 6554
Columns: water
precision recall f1-score support
0 0.95 1.00 0.97 6117
1 0.88 0.21 0.34 437
accuracy 0.95 6554
macro avg 0.91 0.60 0.65 6554
weighted avg 0.94 0.95 0.93 6554
Columns: food
precision recall f1-score support
0 0.93 0.99 0.96 5843
1 0.86 0.42 0.56 711
accuracy 0.93 6554
macro avg 0.90 0.70 0.76 6554
weighted avg 0.93 0.93 0.92 6554
Columns: shelter
precision recall f1-score support
0 0.93 1.00 0.96 5978
1 0.84 0.21 0.33 576
accuracy 0.93 6554
macro avg 0.88 0.60 0.65 6554
weighted avg 0.92 0.93 0.91 6554
Columns: clothing
precision recall f1-score support
0 0.98 1.00 0.99 6442
1 0.73 0.07 0.13 112
accuracy 0.98 6554
macro avg 0.86 0.54 0.56 6554
weighted avg 0.98 0.98 0.98 6554
Columns: money
precision recall f1-score support
0 0.98 1.00 0.99 6407
1 1.00 0.02 0.04 147
accuracy 0.98 6554
macro avg 0.99 0.51 0.51 6554
weighted avg 0.98 0.98 0.97 6554
Columns: missing_people
precision recall f1-score support
0 0.99 1.00 0.99 6486
1 1.00 0.01 0.03 68
accuracy 0.99 6554
macro avg 0.99 0.51 0.51 6554
weighted avg 0.99 0.99 0.98 6554
Columns: refugees
| MIT | models/ML Pipeline Preparation.ipynb | thiagofuruchima/disaster_message_classification |
8. Try improving your model further. Here are a few ideas:* try other machine learning algorithms* add other features besides the TF-IDF | # TODO: Model is taking too long to fit
# I have to find a better engine to process it,
# before testing new ideas | _____no_output_____ | MIT | models/ML Pipeline Preparation.ipynb | thiagofuruchima/disaster_message_classification |
9. Export your model as a pickle file | # Save the model to pickl file
with open("DISASSTER_MODEL.pkl", 'wb') as file:
file.write(pickle.dumps(cv)) | _____no_output_____ | MIT | models/ML Pipeline Preparation.ipynb | thiagofuruchima/disaster_message_classification |
Examples and Exercises from Think Stats, 2nd Editionhttp://thinkstats2.comCopyright 2016 Allen B. DowneyMIT License: https://opensource.org/licenses/MIT | from __future__ import print_function, division
%matplotlib inline
import numpy as np
import brfss
import thinkstats2
import thinkplot | _____no_output_____ | MIT | DSC 530 - Data Exploration and Analysis/ThinkStats2/solutions/chap06soln.ipynb | Hakuna-Patata/BU_MSDS_PTW |
I'll start with the data from the BRFSS again. | df = brfss.ReadBrfss(nrows=None) | _____no_output_____ | MIT | DSC 530 - Data Exploration and Analysis/ThinkStats2/solutions/chap06soln.ipynb | Hakuna-Patata/BU_MSDS_PTW |
Here are the mean and standard deviation of female height in cm. | female = df[df.sex==2]
female_heights = female.htm3.dropna()
mean, std = female_heights.mean(), female_heights.std()
mean, std | _____no_output_____ | MIT | DSC 530 - Data Exploration and Analysis/ThinkStats2/solutions/chap06soln.ipynb | Hakuna-Patata/BU_MSDS_PTW |
`NormalPdf` returns a Pdf object that represents the normal distribution with the given parameters.`Density` returns a probability density, which doesn't mean much by itself. | pdf = thinkstats2.NormalPdf(mean, std)
pdf.Density(mean + std) | _____no_output_____ | MIT | DSC 530 - Data Exploration and Analysis/ThinkStats2/solutions/chap06soln.ipynb | Hakuna-Patata/BU_MSDS_PTW |
`thinkplot` provides `Pdf`, which plots the probability density with a smooth curve. | thinkplot.Pdf(pdf, label='normal')
thinkplot.Config(xlabel='x', ylabel='PDF', xlim=[140, 186]) | _____no_output_____ | MIT | DSC 530 - Data Exploration and Analysis/ThinkStats2/solutions/chap06soln.ipynb | Hakuna-Patata/BU_MSDS_PTW |
`Pdf` provides `MakePmf`, which returns a `Pmf` object that approximates the `Pdf`. | pmf = pdf.MakePmf()
thinkplot.Pmf(pmf, label='normal')
thinkplot.Config(xlabel='x', ylabel='PDF', xlim=[140, 186]) | _____no_output_____ | MIT | DSC 530 - Data Exploration and Analysis/ThinkStats2/solutions/chap06soln.ipynb | Hakuna-Patata/BU_MSDS_PTW |
If you have a `Pmf`, you can also plot it using `Pdf`, if you have reason to think it should be represented as a smooth curve. | thinkplot.Pdf(pmf, label='normal')
thinkplot.Config(xlabel='x', ylabel='PDF', xlim=[140, 186]) | _____no_output_____ | MIT | DSC 530 - Data Exploration and Analysis/ThinkStats2/solutions/chap06soln.ipynb | Hakuna-Patata/BU_MSDS_PTW |
Using a sample from the actual distribution, we can estimate the PDF using Kernel Density Estimation (KDE).If you run this a few times, you'll see how much variation there is in the estimate. | thinkplot.Pdf(pdf, label='normal')
sample = np.random.normal(mean, std, 500)
sample_pdf = thinkstats2.EstimatedPdf(sample, label='sample')
thinkplot.Pdf(sample_pdf, label='sample KDE')
thinkplot.Config(xlabel='x', ylabel='PDF', xlim=[140, 186]) | _____no_output_____ | MIT | DSC 530 - Data Exploration and Analysis/ThinkStats2/solutions/chap06soln.ipynb | Hakuna-Patata/BU_MSDS_PTW |
MomentsRaw moments are just sums of powers. | def RawMoment(xs, k):
return sum(x**k for x in xs) / len(xs) | _____no_output_____ | MIT | DSC 530 - Data Exploration and Analysis/ThinkStats2/solutions/chap06soln.ipynb | Hakuna-Patata/BU_MSDS_PTW |
The first raw moment is the mean. The other raw moments don't mean much. | RawMoment(female_heights, 1), RawMoment(female_heights, 2), RawMoment(female_heights, 3)
def Mean(xs):
return RawMoment(xs, 1)
Mean(female_heights) | _____no_output_____ | MIT | DSC 530 - Data Exploration and Analysis/ThinkStats2/solutions/chap06soln.ipynb | Hakuna-Patata/BU_MSDS_PTW |
The central moments are powers of distances from the mean. | def CentralMoment(xs, k):
mean = RawMoment(xs, 1)
return sum((x - mean)**k for x in xs) / len(xs) | _____no_output_____ | MIT | DSC 530 - Data Exploration and Analysis/ThinkStats2/solutions/chap06soln.ipynb | Hakuna-Patata/BU_MSDS_PTW |
The first central moment is approximately 0. The second central moment is the variance. | CentralMoment(female_heights, 1), CentralMoment(female_heights, 2), CentralMoment(female_heights, 3)
def Var(xs):
return CentralMoment(xs, 2)
Var(female_heights) | _____no_output_____ | MIT | DSC 530 - Data Exploration and Analysis/ThinkStats2/solutions/chap06soln.ipynb | Hakuna-Patata/BU_MSDS_PTW |
The standardized moments are ratios of central moments, with powers chosen to make the dimensions cancel. | def StandardizedMoment(xs, k):
var = CentralMoment(xs, 2)
std = np.sqrt(var)
return CentralMoment(xs, k) / std**k | _____no_output_____ | MIT | DSC 530 - Data Exploration and Analysis/ThinkStats2/solutions/chap06soln.ipynb | Hakuna-Patata/BU_MSDS_PTW |
The third standardized moment is skewness. | StandardizedMoment(female_heights, 1), StandardizedMoment(female_heights, 2), StandardizedMoment(female_heights, 3)
def Skewness(xs):
return StandardizedMoment(xs, 3)
Skewness(female_heights) | _____no_output_____ | MIT | DSC 530 - Data Exploration and Analysis/ThinkStats2/solutions/chap06soln.ipynb | Hakuna-Patata/BU_MSDS_PTW |
Normally a negative skewness indicates that the distribution has a longer tail on the left. In that case, the mean is usually less than the median. | def Median(xs):
cdf = thinkstats2.Cdf(xs)
return cdf.Value(0.5) | _____no_output_____ | MIT | DSC 530 - Data Exploration and Analysis/ThinkStats2/solutions/chap06soln.ipynb | Hakuna-Patata/BU_MSDS_PTW |
But in this case the mean is greater than the median, which indicates skew to the right. | Mean(female_heights), Median(female_heights) | _____no_output_____ | MIT | DSC 530 - Data Exploration and Analysis/ThinkStats2/solutions/chap06soln.ipynb | Hakuna-Patata/BU_MSDS_PTW |
Because the skewness is based on the third moment, it is not robust; that is, it depends strongly on a few outliers. Pearson's median skewness is more robust. | def PearsonMedianSkewness(xs):
median = Median(xs)
mean = RawMoment(xs, 1)
var = CentralMoment(xs, 2)
std = np.sqrt(var)
gp = 3 * (mean - median) / std
return gp | _____no_output_____ | MIT | DSC 530 - Data Exploration and Analysis/ThinkStats2/solutions/chap06soln.ipynb | Hakuna-Patata/BU_MSDS_PTW |
Pearson's skewness is positive, indicating that the distribution of female heights is slightly skewed to the right. | PearsonMedianSkewness(female_heights) | _____no_output_____ | MIT | DSC 530 - Data Exploration and Analysis/ThinkStats2/solutions/chap06soln.ipynb | Hakuna-Patata/BU_MSDS_PTW |
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