From 25c4e150382089662289fc0cb8d3631a0d34f4c5 Mon Sep 17 00:00:00 2001 From: Roni Kobrosly Date: Mon, 4 Jan 2021 09:59:37 -0500 Subject: [PATCH] update to docs (#31) Co-authored-by: rkobrosly --- causal_curve/core.py | 2 +- docs/TMLE_Regressor.rst | 62 +++++++++++++++++++++++++++++++++------- docs/changelog.rst | 7 ++++- docs/conf.py | 2 +- imgs/tmle_plot.png | Bin 0 -> 319011 bytes setup.py | 2 +- 6 files changed, 60 insertions(+), 15 deletions(-) create mode 100644 imgs/tmle_plot.png diff --git a/causal_curve/core.py b/causal_curve/core.py index 7efc551..8838de1 100644 --- a/causal_curve/core.py +++ b/causal_curve/core.py @@ -10,7 +10,7 @@ class Core: """Base class for causal_curve module""" def __init__(self): - __version__ = "1.0.0" + __version__ = "1.0.1" def get_params(self): """Returns a dict of all of the object's user-facing parameters diff --git a/docs/TMLE_Regressor.rst b/docs/TMLE_Regressor.rst index 7948faa..0243301 100644 --- a/docs/TMLE_Regressor.rst +++ b/docs/TMLE_Regressor.rst @@ -18,24 +18,64 @@ and produces significantly smaller confidence intervals. However it is less comp and will take longer to run. In addition, **the treatment values provided should be roughly normally-distributed**, otherwise you may encounter internal math errors. +Let's first generate some simple toy data: ->>> df.head(5) # a pandas dataframe with your data - X_1 X_2 Treatment Outcome -0 0.596685 0.162688 0.000039 12.3 -1 1.014187 0.916101 0.000197 14.9 -2 0.932859 1.328576 0.000223 19.01 -3 1.140052 0.555203 0.000339 22.3 -4 1.613471 0.340886 0.000438 24.98 + +>>> import matplotlib.pyplot as plt +import numpy as np +import pandas as pd +from causal_curve import TMLE_Regressor +np.random.seed(200) + +>>> def generate_data(t, A, sigma, omega, noise=0, n_outliers=0, random_state=0): + y = A * np.exp(-sigma * t) * np.sin(omega * t) + rnd = np.random.RandomState(random_state) + error = noise * rnd.randn(t.size) + outliers = rnd.randint(0, t.size, n_outliers) + error[outliers] *= 35 + return y + error + +>>> treatment = np.linspace(0, 10, 1000) +outcome = generate_data( + t = treatment, + A = 2, + sigma = 0.1, + omega = (0.1 * 2 * np.pi), + noise = 0.1, + n_outliers = 5 +) +x1 = np.random.uniform(0,10,1000) +x2 = (np.random.uniform(0,10,1000) * 3) + +>>> df = pd.DataFrame( + { + 'x1': x1, + 'x2': x2, + 'treatment': treatment, + 'outcome': outcome + } +) + + +All we do now is employ the TMLE_Regressor class, with mostly default settings: >>> from causal_curve import TMLE_Regressor ->>> tmle = TMLE_Regressor( - random_seed=111, - verbose=True, +tmle = TMLE_Regressor( + random_seed=111 ) >>> tmle.fit(T = df['Treatment'], X = df[['X_1', 'X_2']], y = df['Outcome']) ->>> gps_results = tmle.calculate_CDRC(0.99) +gps_results = tmle.calculate_CDRC(0.99) + +The resulting dataframe contains all of the data you need to generate the following plot: + +.. image:: ../imgs/tmle_plot.png + +To generate user-specified points along the curve, use the ``point_estimate`` and ``point_estimate_interval`` methods: + +>>> tmle.point_estimate(np.array([5.5])) +tmle.point_estimate_interval(np.array([5.5])) References diff --git a/docs/changelog.rst b/docs/changelog.rst index 057e634..ce9264c 100644 --- a/docs/changelog.rst +++ b/docs/changelog.rst @@ -5,8 +5,13 @@ Change Log ========== -Version 1.0.0: **Major Update** +Version 1.0.1 ------------- +- Added to TMLE overview in the docs (including plot) + + +Version 1.0.0: **Major Update** +------------------------------- - Overhaul of the TMLE tool to make it dramatically more accurate and user-friendly. - Improved TMLE example documentation - Much like with `scikit-learn`, there are now separate model classes used for predicting binary or continuous outcomes diff --git a/docs/conf.py b/docs/conf.py index 9c8461a..5d068a7 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -23,7 +23,7 @@ author = "Roni Kobrosly" # The full version, including alpha/beta/rc tags -release = "1.0.0" +release = "1.0.1" # -- General configuration --------------------------------------------------- diff --git a/imgs/tmle_plot.png b/imgs/tmle_plot.png new file mode 100644 index 0000000000000000000000000000000000000000..e5397fed88b53e9896af00186dd00f1bfb6dc47b GIT binary patch literal 319011 zcmeFZd038H8#bPK3{jy$rbrXf;HhXwrJ^*KG-#AcX&y8nMY9G8B?=81R2roujVRHm ziKZvbb7}m}#k=3Vf5-71@87>azV@*Xdy}5~Uh7)dbzbLrUdvlS{uJ$6rnNLQG_*3( z5=t~Q8yab7))=f_iQlPS^5G?Zq1Z}l*eY8Z+dAl38_~$=*r};CdHC{3s zY%)u{aPp;rrG|pI;?JFzTg65nMDg{1>>vEd-8GxTn@p7%ygFnP#%gRYWHV^6Z}knD z-T&h&+U0$7&6-Vg|KsbDzhtJw|M&_WdRZ2B`+t0mvNASq{vRKpm|wK}{Qr2y-4zZR zhNu6>6G;5p;A`&vKc0Z2O)rS&e>?$Qmk4(n`~P@^XqQ|6ZxR0AA|OKk|F{UDRf*@b zHW(Wl3%fVBw8%y7rVHu~U=!nY=H)+f;BbCr2=>OCnhGE zmzMf7uiCnGtAG0L3LRtPeU*=n`F@Q(yN92j-eIV<*kjek;hBj(_fP2=8Is zx4EM(uC8*CUI_uLA}iV0*djZytIjDD+a85P?bmg;6GKOS#zyHE`!G0*@#5JA2M47} zbzhm#=^GeGQe8%hw{ob&-KkEk=^h*mxV4e(fiSI%S$%Tx=O-uSB3B{<{?u#9wVNI; z+GrQ>Iw$8xp~tFk>=F`&ORe+Wzwx@)c5{Dk`4NGY4+NV})Fna26^F|Mzk`|jEr8Pq4Y8J2=bE| zrnR5*oNR3S8|aeGXQ_3~O@1$2=IH2nyHc@t_5r?4B~D?<*{I{6?D2tJ+bGsgtF8V7 zr}aAf|EB(mJ@$jm-bNq73R_!S4JmlOl}(wKkDPP)KD7IM{JWYbCvSE8{8Ou<^LF#{ zZcu)HrkZlxu2+Szp?mk&=W-`CG&C{|1Waqn?y?FU)Xn!R582;=)ssrO8y4nu?HbMU z<;x#S`7>$cIa)nx)2gXix^(Fwm+32_?h9H&rCy$%g;iA>Sw-yMTNccFZDyB^yYs~2 z{{8#g1WZFKX>}NRG~X{hPlq)eo|-DHxXWd=Fy~ZUzjgVN`ef~*46}Nys5L#e#wP98 z_Qk_LimJ1%42nB06;@Vmz@nDkrrVRo8YOJU={z;)zgyDBBx3vTx)^jR{GsrYzbl}U z)QJ;I?FYUYePOX5YE7yi9R52-B|^nMK00>(;>h`*U!FDc9o*_P*}ol|7HiX7Rdu$= z-0$9l2R>C|i!2kpwaQQ5PP+d1@q--DmFu@%o1S(ou8%FKI*3OTh(BF7Yk z&0l{6$142({hxE&g>B>FmG_d@kJjq{`88G})BJwrh*d{P;kR#^MSf-5u3x_{EMl=V z=SA7%V9RThhL^j-xX(p92Q_zpdibNO%d@XO{4bDM9L3nRZ;}qpH zXZ$MXE}NUbbR5x_8!fwR-`{YZQ#Ij2Z%zE|fB?aBJJgf43K1WQNty!YExEOZPTSsn z`LemAG_anMZ(7SK>NclQ^miux7YuPK`%!djWc;Uc$a}Yixg5^db6fs~IEsUUS0~Mp zstiTco=Ve^#p}RJymlC>qo^TY)uia^7MZViK$7IY^kwq{ z6@H9NecR8E-%fZj+jUkD-?hrp((>l5TMva<8 zIZ3OjF$!8Vn*=vJ6uf)_rA0>npSP+U$Dy8^Y_b$CJ>}2jxpc)^lW@B2dnlCcrvg~2 zDXsC!`>~yu!h`hQ6&3yH>+_{nMValGMfxyl$rVV}d6VGOpVx`)ou2Lpl%&#gD9}W? 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